diff --git a/examples/configs/distillation_math.yaml b/examples/configs/distillation_math.yaml
index 90e16000aa8..76ccf33e0ce 100644
--- a/examples/configs/distillation_math.yaml
+++ b/examples/configs/distillation_math.yaml
@@ -201,6 +201,9 @@ policy: &POLICY_BASE
         temperature: 1.0
         top_p: 1.0
         top_k: null
+        val_temperature: ${.temperature}
+        val_top_p: ${.top_p}
+        val_top_k: ${.top_k}
         stop_token_ids: null
         stop_strings: null
         vllm_cfg:
diff --git a/examples/configs/evals/eval.yaml b/examples/configs/evals/eval.yaml
index c492ac34cd1..820d503f27e 100644
--- a/examples/configs/evals/eval.yaml
+++ b/examples/configs/evals/eval.yaml
@@ -12,6 +12,9 @@ generation:
   temperature: 0.0
   top_p: 1.0
   top_k: -1 # -1 means disable
+  val_temperature: ${.temperature}
+  val_top_p: ${.top_p}
+  val_top_k: ${.top_k}
   num_prompts_per_step: -1 # -1 means pass all prompts at once
   model_name: "Qwen/Qwen2.5-Math-1.5B-Instruct"
   stop_token_ids: null
diff --git a/examples/configs/evals/mmau.yaml b/examples/configs/evals/mmau.yaml
index e12c3ea0aec..7bc324d492f 100644
--- a/examples/configs/evals/mmau.yaml
+++ b/examples/configs/evals/mmau.yaml
@@ -11,6 +11,9 @@ generation:
   temperature: 0.0
   top_p: 1.0
   top_k: -1
+  val_temperature: ${.temperature}
+  val_top_p: ${.top_p}
+  val_top_k: ${.top_k}
   num_prompts_per_step: -1
   model_name: "Qwen/Qwen2.5-Omni-3B"
   stop_token_ids: null
diff --git a/examples/configs/grpo_math_1B.yaml b/examples/configs/grpo_math_1B.yaml
index c4682a2c976..062abddaa4c 100644
--- a/examples/configs/grpo_math_1B.yaml
+++ b/examples/configs/grpo_math_1B.yaml
@@ -15,6 +15,9 @@ grpo:
   advantage_clip_low: null
   advantage_clip_high: null
   max_val_samples: 256
+  # Validation rollouts per prompt; k > 1 also reports the pass_k metric.
+  # max_val_samples counts PROMPTS: total validation rollouts = max_val_samples * k.
+  val_num_generations_per_prompt: 1
   # Early stop once this metric (e.g. accuracy or pass_k) reaches the threshold; null disables.
   stop_at_validation_metric: null
   # Required when stop_at_validation_metric is set.
@@ -344,6 +347,10 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    # Validation-only sampling; defaults follow the train values above.
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # null = topology default (IPC colocated, NCCL non-colocated).
diff --git a/examples/configs/ppo_math_1B.yaml b/examples/configs/ppo_math_1B.yaml
index 1d79963534b..2cfb53e3263 100644
--- a/examples/configs/ppo_math_1B.yaml
+++ b/examples/configs/ppo_math_1B.yaml
@@ -234,6 +234,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     mcore_generation_config:
diff --git a/examples/configs/recipes/llm/mopd-qwen3-1.7b-3n8g-megatron-pack.yaml b/examples/configs/recipes/llm/mopd-qwen3-1.7b-3n8g-megatron-pack.yaml
index d967ea86519..e8be20b666c 100644
--- a/examples/configs/recipes/llm/mopd-qwen3-1.7b-3n8g-megatron-pack.yaml
+++ b/examples/configs/recipes/llm/mopd-qwen3-1.7b-3n8g-megatron-pack.yaml
@@ -2,7 +2,6 @@ defaults: ../../grpo_math_1B.yaml
 grpo:
   num_prompts_per_step: 8
   num_generations_per_prompt: 4
-  num_val_generations_per_prompt: 1
   max_num_steps: 5
   val_period: 1000
   overlong_filtering: true
diff --git a/examples/nemo_gym/grpo_nanov3.yaml b/examples/nemo_gym/grpo_nanov3.yaml
index 8fa3175d9e4..103d470f610 100644
--- a/examples/nemo_gym/grpo_nanov3.yaml
+++ b/examples/nemo_gym/grpo_nanov3.yaml
@@ -2,7 +2,7 @@
 grpo:
   num_prompts_per_step: 128
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 4
+  val_num_generations_per_prompt: 4
   max_rollout_turns: 1 # for multi-turn rollouts. Math Environments just have 1 turn (answering the question)
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -210,6 +210,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     mcore_generation_config:
diff --git a/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe1.yaml b/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe1.yaml
index 48f284cb711..c19fb351b65 100644
--- a/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe1.yaml
+++ b/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe1.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 64
   num_generations_per_prompt: 8
-  num_val_generations_per_prompt: 1
+  val_num_generations_per_prompt: 1
   max_num_epochs: 100
   advantage_clip_low: -100
   advantage_clip_high: 100
diff --git a/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe2.yaml b/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe2.yaml
index 6764b43126d..449b873e5f2 100644
--- a/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe2.yaml
+++ b/examples/nemo_gym/grpo_qwen3_30ba3b_thinking_swe2.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 8
   num_generations_per_prompt: 8
-  num_val_generations_per_prompt: 1
+  val_num_generations_per_prompt: 1
   max_num_epochs: 100
   advantage_clip_low: -100
   advantage_clip_high: 100
diff --git a/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml b/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml
index e7b78207ff4..b3641dd5437 100644
--- a/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml
+++ b/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml
@@ -14,6 +14,7 @@ grpo:
   advantage_clip_low: null
   advantage_clip_high: null
   max_val_samples: null   # inferred from size of val dataset. for multi evals, repeat val ds via `num_repeats` in `ng_prepare_data`.
+  val_num_generations_per_prompt: 1
   # Early stop once this metric (e.g. accuracy or pass_k) reaches the threshold; null disables.
   stop_at_validation_metric: null
   # Required when stop_at_validation_metric is set.
@@ -226,6 +227,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/examples/nemo_gym/nemotron-3-super/stage1_rlvr.yaml b/examples/nemo_gym/nemotron-3-super/stage1_rlvr.yaml
index 1b2d300dc26..03317acd30e 100644
--- a/examples/nemo_gym/nemotron-3-super/stage1_rlvr.yaml
+++ b/examples/nemo_gym/nemotron-3-super/stage1_rlvr.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 256
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -226,6 +226,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/examples/nemo_gym/nemotron-3-super/stage2_swe1.yaml b/examples/nemo_gym/nemotron-3-super/stage2_swe1.yaml
index 59c41c3d0b2..8a8d52ca042 100644
--- a/examples/nemo_gym/nemotron-3-super/stage2_swe1.yaml
+++ b/examples/nemo_gym/nemotron-3-super/stage2_swe1.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 64
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 1
+  val_num_generations_per_prompt: 1
   max_rollout_turns: 1
   max_num_epochs: 100
   max_num_steps: 1000000
@@ -226,6 +226,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/examples/nemo_gym/nemotron-3-super/stage2_swe2.yaml b/examples/nemo_gym/nemotron-3-super/stage2_swe2.yaml
index 72a22c7913d..4c5284f5aae 100644
--- a/examples/nemo_gym/nemotron-3-super/stage2_swe2.yaml
+++ b/examples/nemo_gym/nemotron-3-super/stage2_swe2.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 16
   num_generations_per_prompt: 32
-  num_val_generations_per_prompt: 1
+  val_num_generations_per_prompt: 1
   max_rollout_turns: 1
   max_num_epochs: 100
   max_num_steps: 1000000
@@ -219,6 +219,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/examples/nemo_gym/nemotron-3-super/stage3_rlhf.yaml b/examples/nemo_gym/nemotron-3-super/stage3_rlhf.yaml
index e56c770a2b4..f2d2b38930f 100644
--- a/examples/nemo_gym/nemotron-3-super/stage3_rlhf.yaml
+++ b/examples/nemo_gym/nemotron-3-super/stage3_rlhf.yaml
@@ -13,7 +13,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 128
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -226,6 +226,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/examples/nemo_gym/nemotron-3-ultra/ifbench_teacher.yaml b/examples/nemo_gym/nemotron-3-ultra/ifbench_teacher.yaml
index 026824cf562..18dc57404a5 100644
--- a/examples/nemo_gym/nemotron-3-ultra/ifbench_teacher.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/ifbench_teacher.yaml
@@ -47,7 +47,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 128
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -305,6 +305,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/mopd.yaml b/examples/nemo_gym/nemotron-3-ultra/mopd.yaml
index fe85213fdd4..203bf03e83f 100644
--- a/examples/nemo_gym/nemotron-3-ultra/mopd.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/mopd.yaml
@@ -62,7 +62,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 1024
   num_generations_per_prompt: 1
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -326,6 +326,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/reasoning_teacher.yaml b/examples/nemo_gym/nemotron-3-ultra/reasoning_teacher.yaml
index b32979ce39a..e62f261e867 100644
--- a/examples/nemo_gym/nemotron-3-ultra/reasoning_teacher.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/reasoning_teacher.yaml
@@ -51,7 +51,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 128
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 10
   max_num_steps: 1000000
@@ -308,6 +308,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/rlhf_teacher.yaml b/examples/nemo_gym/nemotron-3-ultra/rlhf_teacher.yaml
index 32fc72deb4b..d757cde7a0e 100644
--- a/examples/nemo_gym/nemotron-3-ultra/rlhf_teacher.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/rlhf_teacher.yaml
@@ -48,7 +48,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 128
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -306,6 +306,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/student_rlvr1.yaml b/examples/nemo_gym/nemotron-3-ultra/student_rlvr1.yaml
index 03e43b694dc..f6c3b308ad5 100644
--- a/examples/nemo_gym/nemotron-3-ultra/student_rlvr1.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/student_rlvr1.yaml
@@ -44,7 +44,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 512
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -302,6 +302,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/student_rlvr2.yaml b/examples/nemo_gym/nemotron-3-ultra/student_rlvr2.yaml
index 3a9fbae3712..262d1679cd8 100644
--- a/examples/nemo_gym/nemotron-3-ultra/student_rlvr2.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/student_rlvr2.yaml
@@ -45,7 +45,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 512
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 1
   max_num_steps: 1000000
@@ -303,6 +303,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # TP=8 EP=8: EP=TP so vllm_dp_size=1, async_engine=true works with NeMo Gym.
diff --git a/examples/nemo_gym/nemotron-3-ultra/swe_teacher.yaml b/examples/nemo_gym/nemotron-3-ultra/swe_teacher.yaml
index c8db114be15..75224b0e556 100644
--- a/examples/nemo_gym/nemotron-3-ultra/swe_teacher.yaml
+++ b/examples/nemo_gym/nemotron-3-ultra/swe_teacher.yaml
@@ -67,7 +67,7 @@ checkpointing:
 grpo:
   num_prompts_per_step: 32
   num_generations_per_prompt: 16
-  num_val_generations_per_prompt: 2
+  val_num_generations_per_prompt: 2
   max_rollout_turns: 1
   max_num_epochs: 4
   max_num_steps: 1000000
@@ -325,6 +325,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     vllm_cfg:
diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py
index 4159e5d5b60..ab4cdaea80a 100644
--- a/nemo_rl/algorithms/grpo.py
+++ b/nemo_rl/algorithms/grpo.py
@@ -98,6 +98,7 @@
 from nemo_rl.models.generation.interfaces import (
     GenerationConfig,
     GenerationInterface,
+    GenerationSamplingParams,
     resolve_routed_experts_dtype_name_for_model,
 )
 from nemo_rl.models.generation.megatron import MegatronGeneration
@@ -246,7 +247,13 @@ class GRPOConfig(BaseModel, extra="allow"):
     # Whether to run validation on the last training step. Setting this to True ensures the
     # final checkpoint has validation metrics, which is required for get_best_checkpoint_path().
     val_at_end: bool = False
+    # Counts PROMPTS, not rollouts: with val_num_generations_per_prompt = k,
+    # total validation rollouts = max_val_samples * k.
     max_val_samples: int | None = 256  # None for NeMo-Gym compatibility
+    # Number of independent validation rollouts generated for each prompt;
+    # k > 1 additionally reports pass@k over each prompt's k rollouts as the
+    # pass_k metric.
+    val_num_generations_per_prompt: int = 1
     # Early stop: end training once this validation metric (e.g. accuracy,
     # always reported, or pass_k with grouped validation) reaches
     # stop_at_validation_threshold; null disables early stopping.
@@ -398,6 +405,40 @@ def setup(
     if generation_config["backend"] == "vllm":
         normalize_vllm_refit_config(cast(VllmConfig, generation_config))
 
+    # Validation-only sampling is honored only on the NeMo-Gym vLLM rollout
+    # path; everywhere else validation must sample exactly like training.
+    val_sampling_overridden = (
+        generation_config["val_temperature"] != generation_config["temperature"]
+        or generation_config["val_top_p"] != generation_config["top_p"]
+        or generation_config["val_top_k"] != generation_config["top_k"]
+    )
+    if val_sampling_overridden:
+        assert generation_config["backend"] == "vllm" and _should_use_nemo_gym(
+            master_config
+        ), (
+            "generation.val_temperature/val_top_p/val_top_k differing from the "
+            "train sampling params is only supported for vLLM NeMo-Gym rollouts."
+        )
+        # The NeMo-Gym path only stamps temperature/top_p onto requests and
+        # rejects any top_k at rollout time, so a val_top_k override can never
+        # be honored — fail here instead of at the first validation step.
+        assert not generation_config["val_top_k"], (
+            "generation.val_top_k is not supported: the NeMo-Gym rollout path "
+            "only honors val_temperature/val_top_p. Leave val_top_k null."
+        )
+    assert grpo_config.val_num_generations_per_prompt >= 1, (
+        "grpo.val_num_generations_per_prompt must be >= 1"
+    )
+    # pass_k is only reported when k > 1; catch the mismatch here instead of
+    # at the first validation step.
+    assert not (
+        grpo_config.stop_at_validation_metric == "pass_k"
+        and grpo_config.val_num_generations_per_prompt <= 1
+    ), (
+        "grpo.stop_at_validation_metric='pass_k' requires "
+        "grpo.val_num_generations_per_prompt > 1"
+    )
+
     # Set seed for all random number generators
     set_seed(grpo_config.seed)
 
@@ -3681,6 +3722,10 @@ def validate(
     timer = Timer(context={"worker": "validator"})
     with timer.time("total_validation_time"):
         print(f"▶ Starting validation at step {step}...", flush=True)
+        # >= 1 is validated in setup().
+        val_num_generations_per_prompt = (
+            master_config.grpo.val_num_generations_per_prompt
+        )
 
         total_rewards = []
         total_lengths = []
@@ -3693,12 +3738,23 @@ def validate(
             if batch_idx >= max_batches:
                 break
 
+            if val_num_generations_per_prompt > 1:
+                val_batch = val_batch.repeat_interleave(val_num_generations_per_prompt)
+
             additional_metrics_to_report = dict()
             # Generate responses (updates the LLMMessageLogType in batch_with_msg_logs)
             # Use async rollouts when enabled by config/backend defaults.
             # We cascade NeMo-Gym first since NeMo-Gym also uses async rollouts.
             if _should_use_nemo_gym(master_config):
                 generation_config = master_config.policy["generation"]
+                # Validation-only sampling (e.g. near-greedy validation);
+                # defaults to the train profile via the exemplar YAML
+                # interpolations. Training rollouts keep policy.generation.
+                val_sampling_params = GenerationSamplingParams(
+                    temperature=generation_config["val_temperature"],
+                    top_p=generation_config["val_top_p"],
+                    top_k=generation_config["val_top_k"],
+                )
                 nemo_gym_rollout_result = run_nemo_gym_rollout_sync(
                     policy_generation=policy_generation,
                     input_batch=val_batch,
@@ -3706,6 +3762,7 @@ def validate(
                     task_to_env=val_task_to_env,
                     max_seq_len=master_config.policy["max_total_sequence_length"],
                     generation_config=generation_config,
+                    sampling_params=val_sampling_params,
                     log_full_result_tables=should_log_nemo_gym_full_result_tables(
                         wandb_enabled=master_config.logger["wandb_enabled"],
                         wandb_config=master_config.logger["wandb"],
@@ -3756,11 +3813,26 @@ def validate(
 
             all_message_logs.extend(to_env)
 
-        # Calculate validation metrics
+        # Calculate validation metrics. accuracy is the mean reward over all
+        # rollouts; grouped validation (val_num_generations_per_prompt > 1)
+        # additionally reports pass@k over each prompt's k rollouts as pass_k.
         num_samples = len(total_rewards)
+        pass_k = None
         if num_samples > 0:
             rewards_t = torch.tensor(total_rewards, dtype=torch.float32)
             accuracy = rewards_t.mean().item()
+            if val_num_generations_per_prompt > 1:
+                assert num_samples % val_num_generations_per_prompt == 0, (
+                    "Validation rewards must be divisible by "
+                    "grpo.val_num_generations_per_prompt"
+                )
+                pass_k = (
+                    (rewards_t.view(-1, val_num_generations_per_prompt) > 0)
+                    .any(dim=1)
+                    .float()
+                    .mean()
+                    .item()
+                )
         else:
             accuracy = 0.0
 
@@ -3773,6 +3845,8 @@ def validate(
             "avg_length": avg_length,
             **additional_metrics_to_report,
         }
+        if pass_k is not None:
+            val_metrics["pass_k"] = pass_k
 
         # Print sample conversations only once at the end of validation
         try:
diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py
index 1a6145160b8..84ff46b13d3 100644
--- a/nemo_rl/experience/rollouts.py
+++ b/nemo_rl/experience/rollouts.py
@@ -55,6 +55,7 @@
     GenerationDatumSpec,
     GenerationInterface,
     GenerationOutputSpec,
+    GenerationSamplingParams,
 )
 from nemo_rl.utils.timer import Timer
 
@@ -2007,7 +2008,9 @@ def apply_reward_penalties(
 
 
 def _prepare_nemo_gym_rows(
-    rows: list[dict], generation_config: GenerationConfig
+    rows: list[dict],
+    generation_config: GenerationConfig,
+    sampling_params: GenerationSamplingParams,
 ) -> None:
     """Apply NeMo-RL sampling parameters and stable row indices in place."""
     for row_index, row in enumerate(rows):
@@ -2017,8 +2020,8 @@ def _prepare_nemo_gym_rows(
                 "Each NeMo-Gym row must contain a responses_create_params dict"
             )
 
-        responses_create_params["temperature"] = generation_config["temperature"]
-        responses_create_params["top_p"] = generation_config["top_p"]
+        responses_create_params["temperature"] = sampling_params.temperature
+        responses_create_params["top_p"] = sampling_params.top_p
         configured_max_tokens = generation_config["max_new_tokens"]
         row_max_tokens = responses_create_params.get("max_output_tokens")
         responses_create_params["max_output_tokens"] = (
@@ -2059,6 +2062,7 @@ async def run_async_nemo_gym_rollout(
     thinking_tags: list[str] | tuple[str, ...] | None = None,
     mask_env_flagged_samples: bool = True,
     returns_entire_batch: bool = False,
+    sampling_params: Optional[GenerationSamplingParams] = None,
 ) -> AsyncGenerator[NemoGymRolloutResult, None]:
     """Stream complete NeMo-Gym prompt groups in group-completion order.
 
@@ -2089,6 +2093,9 @@ async def run_async_nemo_gym_rollout(
         returns_entire_batch: Whether to treat the input as one potentially
             heterogeneous group. This requires ``num_generations`` to equal the
             batch size and is used by synchronous callers.
+        sampling_params: Sampling profile stamped onto every NeMo-Gym row.
+            ``None`` uses the train profile from ``generation_config``;
+            validation passes its own profile explicitly.
 
     Yields:
         ``NemoGymRolloutResult`` objects in prompt-group completion order. Rows
@@ -2139,9 +2146,13 @@ async def run_async_nemo_gym_rollout(
     assert not generation_config["stop_token_ids"], (
         "Stop strings is not supported in the generation config in NeMo-Gym path!"
     )
+    if sampling_params is None:
+        sampling_params = GenerationSamplingParams.from_generation_config(
+            generation_config
+        )
     # Top k is not OpenAI compatible, so NeMo-Gym does not guarantee support over it.
-    assert not generation_config["top_k"], (
-        "Top k is not supported in the generation config in NeMo-Gym path!"
+    assert not sampling_params.top_k, (
+        "Top k is not supported in the sampling params in NeMo-Gym path!"
     )
     if num_generations <= 0:
         raise ValueError("num_generations must be greater than zero")
@@ -2162,7 +2173,7 @@ async def run_async_nemo_gym_rollout(
     run_rollouts_timer_label = f"{timer_prefix}/run_rollouts"
 
     with timer.time(total_timer_label):
-        _prepare_nemo_gym_rows(nemo_gym_rows, generation_config)
+        _prepare_nemo_gym_rows(nemo_gym_rows, generation_config, sampling_params)
         accumulator = _NemoGymStreamAccumulator(
             rows=nemo_gym_rows,
             num_generations=num_generations,
@@ -2248,6 +2259,7 @@ def run_nemo_gym_rollout_sync(
     effort_config: Optional[EffortLevelsConfig] = None,
     reward_penalty_config: dict[str, Any] | BaseModel | None = None,
     thinking_tags: list[str] | tuple[str, ...] | None = None,
+    sampling_params: Optional[GenerationSamplingParams] = None,
     mask_env_flagged_samples: bool = True,
 ) -> NemoGymRolloutResult:
     """Run and return one complete NeMo-Gym batch synchronously.
@@ -2271,6 +2283,9 @@ def run_nemo_gym_rollout_sync(
         effort_config: Optional configuration for effort-based reward shaping.
         reward_penalty_config: Optional reward-penalty configuration.
         thinking_tags: Optional opening and closing tags used by thinking penalties.
+        sampling_params: Sampling profile stamped onto every NeMo-Gym row.
+            ``None`` uses the train profile from ``generation_config``;
+            validation passes its own profile explicitly.
         mask_env_flagged_samples: Whether to carry env-driven ``mask_sample``
             flags in the rollout batch for loss masking.
 
@@ -2304,6 +2319,7 @@ async def _consume_rollout() -> NemoGymRolloutResult:
             thinking_tags=thinking_tags,
             mask_env_flagged_samples=mask_env_flagged_samples,
             returns_entire_batch=True,
+            sampling_params=sampling_params,
         ):
             pass
         if rollout_result is None:
diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py
index a757ad3a8ff..791657c394f 100644
--- a/nemo_rl/models/generation/interfaces.py
+++ b/nemo_rl/models/generation/interfaces.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
 from typing import Any, NotRequired, Optional, TypedDict, Union
 
 import ray
@@ -201,6 +202,13 @@ class GenerationConfig(TypedDict):
     temperature: float
     top_p: float
     top_k: int | None
+    # Validation-only sampling. The exemplar YAMLs default these to the train
+    # values above via interpolation (${.temperature}, ...), so validation
+    # samples exactly like training unless overridden. Only honored on the
+    # NeMo-Gym vLLM rollout path (guarded in grpo.setup()).
+    val_temperature: float
+    val_top_p: float
+    val_top_k: int | None
     model_name: NotRequired[str]  # Not Required b/c GRPO writes this
     stop_token_ids: list[int] | None
     stop_strings: list[str] | None
@@ -215,6 +223,33 @@ class GenerationConfig(TypedDict):
     _mtp_weights_from_refit: NotRequired[bool]
 
 
+@dataclass
+class GenerationSamplingParams:
+    """Sampling profile threaded explicitly through rollout entry points.
+
+    Rollout callers construct one from the relevant ``GenerationConfig``
+    fields (train or validation) so the sampling used for a rollout is
+    visible at the call site instead of flowing through config side-channels.
+    Named to distinguish it from ``TrainingSamplingParams`` (train-time logit
+    filtering) and vLLM's own ``SamplingParams``.
+    """
+
+    temperature: float
+    top_p: float
+    top_k: int | None
+
+    @classmethod
+    def from_generation_config(
+        cls, generation_config: "GenerationConfig"
+    ) -> "GenerationSamplingParams":
+        """Build the train-time sampling profile from a generation config."""
+        return cls(
+            temperature=generation_config["temperature"],
+            top_p=generation_config["top_p"],
+            top_k=generation_config["top_k"],
+        )
+
+
 class GenerationDatumSpec(TypedDict):
     """Specification for input data required by generation models.
 
diff --git a/nemo_rl/models/generation/vllm/vllm_worker_async.py b/nemo_rl/models/generation/vllm/vllm_worker_async.py
index 201d0790044..d1da8a3ec1a 100644
--- a/nemo_rl/models/generation/vllm/vllm_worker_async.py
+++ b/nemo_rl/models/generation/vllm/vllm_worker_async.py
@@ -678,9 +678,38 @@ async def create_chat_completion(
             request.top_k = -1
 
             # The request sampling params need to exactly match those as are set in NeMo RL.
-            # If they do not match, the inference will be off policy and destroy training stability.
-            assert request.temperature == generation_config["temperature"]
-            assert request.top_p == generation_config["top_p"]
+            # If they do not match, the inference will be off policy and destroy training
+            # stability. Validation rollouts are the one exception: they are stamped with
+            # the validation sampling profile (generation.val_temperature / val_top_p),
+            # which is metric-only and safe to serve — grpo.validate() is the only
+            # caller that constructs a non-train GenerationSamplingParams. Multi-turn
+            # agents issue their own requests, so this server-side check is the one
+            # chokepoint they all pass.
+            # vLLM resolves an unset top_p from the model's generation_config.json
+            # (ModelConfig.generation_config defaults to "auto"), NOT to 1.0, so a
+            # request omitting it would sample off-policy while passing this check.
+            assert request.top_p is not None, (
+                "top_p must be set explicitly on NeMo-RL requests; an unset top_p is "
+                "resolved by vLLM from the model's generation_config.json and would "
+                "bypass the on-policy sampling check."
+            )
+            request_top_p = request.top_p
+            is_train_sampling = (
+                request.temperature == generation_config["temperature"]
+                and request_top_p == generation_config["top_p"]
+            )
+            is_val_sampling = (
+                request.temperature == generation_config["val_temperature"]
+                and request_top_p == generation_config["val_top_p"]
+            )
+            assert is_train_sampling or is_val_sampling, (
+                f"request sampling (temperature={request.temperature}, "
+                f"top_p={request.top_p}) matches neither the train sampling params "
+                f"(temperature={generation_config['temperature']}, "
+                f"top_p={generation_config['top_p']}) nor the validation sampling "
+                f"params (val_temperature={generation_config['val_temperature']}, "
+                f"val_top_p={generation_config['val_top_p']})"
+            )
 
             try:
                 generator = await openai_serving_chat.create_chat_completion(
diff --git a/research/template_project/configs/grpo_math_1B.yaml b/research/template_project/configs/grpo_math_1B.yaml
index 2862ad17e02..9d7b2b86296 100644
--- a/research/template_project/configs/grpo_math_1B.yaml
+++ b/research/template_project/configs/grpo_math_1B.yaml
@@ -13,6 +13,8 @@ grpo:
   val_at_end: false
   overlong_filtering: false
   max_val_samples: 256
+  # Validation rollouts per prompt; k > 1 also reports the pass_k metric.
+  val_num_generations_per_prompt: 1
   # Early stop once this metric (e.g. accuracy or pass_k) reaches the threshold; null disables.
   stop_at_validation_metric: null
   # Required when stop_at_validation_metric is set.
@@ -286,6 +288,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     mcore_generation_config:
diff --git a/tests/unit/algorithms/test_distillation.py b/tests/unit/algorithms/test_distillation.py
index dfda02093b8..df9188c02b2 100644
--- a/tests/unit/algorithms/test_distillation.py
+++ b/tests/unit/algorithms/test_distillation.py
@@ -150,6 +150,9 @@ def val_iter(self):
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "colocated": {
                         "enabled": False,
                     },
@@ -851,6 +854,9 @@ def test_noncolocated_inference_requires_explicit_gpus_per_node_single_node():
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "backend": "vllm",
                     "colocated": {
                         "enabled": False,  # Non-colocated
@@ -926,6 +932,9 @@ def test_distillation_setup_non_colocated_smoke(monkeypatch, refit_transport):
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "backend": "vllm",
                     "refit_transport": refit_transport,
                     "refit_cfg": None,
@@ -1110,6 +1119,9 @@ def test_distillation_setup_nemo_gym_uses_deferred_vllm(
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "backend": "vllm",
                     "vllm_kwargs": {},
                     "vllm_cfg": {
@@ -1363,6 +1375,9 @@ def test_noncolocated_inference_requires_explicit_gpus_per_node_multi_node():
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "backend": "vllm",
                     "colocated": {
                         "enabled": False,  # Non-colocated
diff --git a/tests/unit/algorithms/test_grpo.py b/tests/unit/algorithms/test_grpo.py
index dee6cdb260b..479e5479919 100644
--- a/tests/unit/algorithms/test_grpo.py
+++ b/tests/unit/algorithms/test_grpo.py
@@ -50,6 +50,7 @@
     dynamic_sampling,
     grpo_train,
     refit_policy_generation,
+    setup,
     validate,
 )
 from nemo_rl.algorithms.grpo_sync import _train_fields_for_step, grpo_train_sync
@@ -269,6 +270,7 @@ def val_iter(self):
                 max_rollout_turns=1,
                 val_period=100,
                 val_start_at=-1,
+                val_num_generations_per_prompt=1,
                 val_batch_size=1,
                 val_at_start=False,
                 val_at_end=False,
@@ -305,6 +307,9 @@ def val_iter(self):
                     "temperature": 1.0,
                     "top_p": 1.0,
                     "top_k": None,
+                    "val_temperature": 1.0,
+                    "val_top_p": 1.0,
+                    "val_top_k": None,
                     "backend": "vllm",
                     "colocated": {"enabled": True},
                     "vllm_cfg": {"async_engine": True},  # Support async mode
@@ -3773,6 +3778,141 @@ def test_validate_works_without_logger(self, mock_grpo_components):
         assert "accuracy" in val_metrics
         assert "avg_length" in val_metrics
 
+    def test_grouped_validation_reports_pass_k(self, mock_grpo_components):
+        mock_batch = BatchedDataDict[DatumSpec](
+            {
+                "message_log": [
+                    [{"role": "user", "content": "a", "token_ids": torch.tensor([1])}],
+                    [{"role": "user", "content": "b", "token_ids": torch.tensor([2])}],
+                ],
+                "task_name": ["math", "math"],
+                "extra_env_info": [{}, {}],
+                "loss_multiplier": torch.tensor([1.0, 1.0]),
+                "idx": torch.tensor([0, 1]),
+                "length": torch.tensor([1, 1]),
+                "total_reward": torch.tensor([0.0, 0.0]),
+            }
+        )
+        mock_dataloader = MagicMock(spec=StatefulDataLoader)
+        mock_dataloader.__iter__ = MagicMock(return_value=iter([mock_batch]))
+        mock_config = mock_grpo_components["master_config"]
+        mock_config.grpo.max_val_samples = 2
+        mock_config.grpo.val_batch_size = 2
+        mock_config.grpo.val_num_generations_per_prompt = 4
+
+        def run_rollout(_policy, repeated_batch, *_args, **_kwargs):
+            # Each prompt is repeated k=4 times, contiguously.
+            assert repeated_batch["idx"].tolist() == [0, 0, 0, 0, 1, 1, 1, 1]
+            # Prompt 0 passes once out of 4; prompt 1 never passes.
+            repeated_batch["total_reward"] = torch.tensor(
+                [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+            )
+            return repeated_batch, {"mean_gen_tokens_per_sample": 1.0}
+
+        with (
+            patch(
+                "nemo_rl.algorithms.grpo.run_multi_turn_rollout",
+                side_effect=run_rollout,
+            ),
+            patch("nemo_rl.algorithms.grpo._should_use_nemo_gym", return_value=False),
+            patch(
+                "nemo_rl.algorithms.grpo._should_use_async_rollouts",
+                return_value=False,
+            ),
+            patch("nemo_rl.algorithms.grpo.print_message_log_samples"),
+        ):
+            val_metrics, _ = validate(
+                MagicMock(),
+                mock_dataloader,
+                MagicMock(),
+                {"math": MagicMock(spec=EnvironmentInterface)},
+                step=0,
+                master_config=mock_config,
+            )
+
+        # accuracy stays the plain mean over all 8 rollouts; pass@4 counts
+        # prompts with at least one passing rollout (1 of 2).
+        assert val_metrics["accuracy"] == pytest.approx(0.125)
+        assert val_metrics["pass_k"] == pytest.approx(0.5)
+
+    def test_validation_uses_val_sampling_params_on_gym_path(
+        self, mock_grpo_components
+    ):
+        mock_batch = BatchedDataDict[DatumSpec](
+            {
+                "message_log": [
+                    [{"role": "user", "content": "a", "token_ids": torch.tensor([1])}],
+                    [{"role": "user", "content": "b", "token_ids": torch.tensor([2])}],
+                ],
+                "task_name": ["math", "math"],
+                "extra_env_info": [{}, {}],
+                "loss_multiplier": torch.tensor([1.0, 1.0]),
+                "idx": torch.tensor([0, 1]),
+                "length": torch.tensor([1, 1]),
+                "total_reward": torch.tensor([0.0, 0.0]),
+            }
+        )
+        mock_dataloader = MagicMock(spec=StatefulDataLoader)
+        mock_dataloader.__iter__ = MagicMock(return_value=iter([mock_batch]))
+        mock_config = mock_grpo_components["master_config"]
+        mock_config.grpo.max_val_samples = 2
+        mock_config.grpo.val_batch_size = 2
+        mock_config.grpo.val_num_generations_per_prompt = 2
+        # Train samples at 1.0/1.0; validation runs near-greedy.
+        mock_config.policy["generation"].update(
+            {"val_temperature": 0.1, "val_top_p": 0.9, "val_top_k": None}
+        )
+        mock_config.logger.update({"wandb_enabled": False, "wandb": {}})
+        mock_config.env = {}
+
+        def run_gym_rollout(**kwargs):
+            repeated_batch = kwargs["input_batch"]
+            # 2 prompts x k=2 validation rollouts, contiguous per prompt.
+            assert repeated_batch["idx"].tolist() == [0, 0, 1, 1]
+            repeated_batch["total_reward"] = torch.tensor([1.0, 0.0, 0.0, 0.0])
+            return MagicMock(
+                final_batch=repeated_batch,
+                rollout_metrics={"mean_gen_tokens_per_sample": 1.0},
+            )
+
+        with (
+            patch(
+                "nemo_rl.algorithms.grpo.run_nemo_gym_rollout_sync",
+                side_effect=run_gym_rollout,
+            ) as mock_rollout,
+            patch("nemo_rl.algorithms.grpo._should_use_nemo_gym", return_value=True),
+            patch("nemo_rl.algorithms.grpo.print_message_log_samples"),
+        ):
+            val_metrics, _ = validate(
+                MagicMock(),
+                mock_dataloader,
+                MagicMock(),
+                {"math": MagicMock(spec=EnvironmentInterface)},
+                step=0,
+                master_config=mock_config,
+            )
+
+        sampling_params = mock_rollout.call_args.kwargs["sampling_params"]
+        assert sampling_params.temperature == pytest.approx(0.1)
+        assert sampling_params.top_p == pytest.approx(0.9)
+        assert sampling_params.top_k is None
+        assert val_metrics["accuracy"] == pytest.approx(0.25)
+        assert val_metrics["pass_k"] == pytest.approx(0.5)
+
+    def test_setup_rejects_val_sampling_outside_gym_vllm_path(
+        self, mock_grpo_components
+    ):
+        master_config = mock_grpo_components["master_config"]
+        # Non-gym rollouts (env has no nemo_gym) with validation sampling
+        # different from training must be rejected at setup time.
+        master_config.policy["generation"].update(
+            {"backend": "megatron", "val_temperature": 0.1}
+        )
+        master_config.env = {}
+
+        with pytest.raises(AssertionError, match="only supported for vLLM NeMo-Gym"):
+            setup(master_config, MagicMock(), MagicMock(), None)
+
     def test_validate_returns_empty_when_no_dataloader(self, mock_grpo_components):
         """Test that validate returns empty dicts when no dataloader is provided."""
         mock_policy_gen = MagicMock()
diff --git a/tests/unit/environments/test_code_environment.py b/tests/unit/environments/test_code_environment.py
index d32550aba1e..cfdb96cc089 100644
--- a/tests/unit/environments/test_code_environment.py
+++ b/tests/unit/environments/test_code_environment.py
@@ -46,6 +46,9 @@
     "temperature": 1.0,
     "top_p": 1.0,
     "top_k": None,
+    "val_temperature": 1.0,
+    "val_top_p": 1.0,
+    "val_top_k": None,
     "stop_token_ids": None,
     "stop_strings": None,
     "vllm_cfg": {
diff --git a/tests/unit/environments/test_retriever.py b/tests/unit/environments/test_retriever.py
index c9413e67590..9e0c27e62f1 100644
--- a/tests/unit/environments/test_retriever.py
+++ b/tests/unit/environments/test_retriever.py
@@ -45,6 +45,9 @@
     "temperature": 1.0,
     "top_p": 1.0,
     "top_k": None,
+    "val_temperature": 1.0,
+    "val_top_p": 1.0,
+    "val_top_k": None,
     "stop_token_ids": None,
     "stop_strings": None,
     "vllm_cfg": {
diff --git a/tests/unit/experience/test_rollouts.py b/tests/unit/experience/test_rollouts.py
index 038384a5958..40676612602 100644
--- a/tests/unit/experience/test_rollouts.py
+++ b/tests/unit/experience/test_rollouts.py
@@ -357,6 +357,9 @@ def initial_multi_step_calculator_batch(rollout_tokenizer):
     "temperature": 0.01,  # Near-greedy
     "top_p": 1.0,
     "top_k": None,
+    "val_temperature": 0.01,
+    "val_top_p": 1.0,
+    "val_top_k": None,
     "stop_token_ids": None,
     "stop_strings": None,
     "vllm_cfg": {
@@ -1188,6 +1191,9 @@ async def _collect():
                 "top_k": None,
                 "temperature": 1.0,
                 "top_p": 1.0,
+                "val_temperature": 1.0,
+                "val_top_p": 1.0,
+                "val_top_k": None,
                 "max_new_tokens": 32,
             },
             num_generations=2,
diff --git a/tests/unit/models/generation/test_vllm_generation.py b/tests/unit/models/generation/test_vllm_generation.py
index 0821f0865b6..93429ccfb5e 100644
--- a/tests/unit/models/generation/test_vllm_generation.py
+++ b/tests/unit/models/generation/test_vllm_generation.py
@@ -67,6 +67,9 @@
     "temperature": 1.0,
     "top_p": 1.0,
     "top_k": None,
+    "val_temperature": 1.0,
+    "val_top_p": 1.0,
+    "val_top_k": None,
     "stop_token_ids": None,
     "stop_strings": None,
     "vllm_cfg": {
diff --git a/tests/unit/models/generation/test_vllm_large_model.py b/tests/unit/models/generation/test_vllm_large_model.py
index 89eaece234c..9b18a446ebf 100644
--- a/tests/unit/models/generation/test_vllm_large_model.py
+++ b/tests/unit/models/generation/test_vllm_large_model.py
@@ -38,6 +38,9 @@
     "temperature": 0.8,
     "top_p": 1.0,
     "top_k": None,
+    "val_temperature": 0.8,
+    "val_top_p": 1.0,
+    "val_top_k": None,
     "stop_token_ids": None,
     "stop_strings": None,
     "vllm_cfg": {
diff --git a/tests/unit/models/generation/test_vllm_quant_backend.py b/tests/unit/models/generation/test_vllm_quant_backend.py
index 6b8bd6487c4..2cb80268661 100644
--- a/tests/unit/models/generation/test_vllm_quant_backend.py
+++ b/tests/unit/models/generation/test_vllm_quant_backend.py
@@ -57,6 +57,9 @@ def _make_vllm_config(tokenizer, *, async_engine=False, is_eval=True):
         "temperature": 0.0,
         "top_p": 1.0,
         "top_k": None,
+        "val_temperature": 0.0,
+        "val_top_p": 1.0,
+        "val_top_k": None,
         "stop_token_ids": None,
         "stop_strings": None,
         "quant_cfg": _QUANT_CFG,
diff --git a/tests/unit/models/generation/trtllm/test_trtllm_generation.py b/tests/unit/models/generation/trtllm/test_trtllm_generation.py
index d9dc1eaea70..1123ebae58a 100644
--- a/tests/unit/models/generation/trtllm/test_trtllm_generation.py
+++ b/tests/unit/models/generation/trtllm/test_trtllm_generation.py
@@ -43,6 +43,9 @@ def _config(**trtllm_overrides):
         "temperature": 1.0,
         "top_p": 1.0,
         "top_k": None,
+        "val_temperature": 1.0,
+        "val_top_p": 1.0,
+        "val_top_k": None,
         "stop_token_ids": None,
         "stop_strings": None,
         "_pad_token_id": 0,
diff --git a/tests/unit/reference_configs/distillation_math.yaml b/tests/unit/reference_configs/distillation_math.yaml
index 5b14bd9bb8b..9fe04653393 100644
--- a/tests/unit/reference_configs/distillation_math.yaml
+++ b/tests/unit/reference_configs/distillation_math.yaml
@@ -191,6 +191,9 @@ policy: &POLICY_BASE
         temperature: 1.0
         top_p: 1.0
         top_k: null
+        val_temperature: ${.temperature}
+        val_top_p: ${.top_p}
+        val_top_k: ${.top_k}
         stop_token_ids: null
         stop_strings: null
         vllm_cfg:
diff --git a/tests/unit/reference_configs/eval.yaml b/tests/unit/reference_configs/eval.yaml
index abe20f4d74d..95813a0a433 100644
--- a/tests/unit/reference_configs/eval.yaml
+++ b/tests/unit/reference_configs/eval.yaml
@@ -12,6 +12,9 @@ generation:
   temperature: 0.0
   top_p: 1.0
   top_k: -1 # -1 means disable
+  val_temperature: ${.temperature}
+  val_top_p: ${.top_p}
+  val_top_k: ${.top_k}
   num_prompts_per_step: -1 # -1 means pass all prompts at once
   model_name: "Qwen/Qwen2.5-Math-1.5B-Instruct"
   stop_token_ids: null
diff --git a/tests/unit/reference_configs/grpo_math_1B.yaml b/tests/unit/reference_configs/grpo_math_1B.yaml
index 5f4ffb2270d..7f026fca478 100644
--- a/tests/unit/reference_configs/grpo_math_1B.yaml
+++ b/tests/unit/reference_configs/grpo_math_1B.yaml
@@ -15,6 +15,7 @@ grpo:
   advantage_clip_low: null
   advantage_clip_high: null
   max_val_samples: 256
+  val_num_generations_per_prompt: 1
   # Early stop once this metric (e.g. accuracy or pass_k) reaches the threshold; null disables.
   stop_at_validation_metric: null
   # Required when stop_at_validation_metric is set.
@@ -340,6 +341,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     # null = topology default (IPC colocated, NCCL non-colocated).
diff --git a/tests/unit/reference_configs/ppo_math_1B_megatron.yaml b/tests/unit/reference_configs/ppo_math_1B_megatron.yaml
index 3610bfd09e8..12ebd6c7cee 100644
--- a/tests/unit/reference_configs/ppo_math_1B_megatron.yaml
+++ b/tests/unit/reference_configs/ppo_math_1B_megatron.yaml
@@ -222,6 +222,9 @@ policy:
     temperature: 1.0
     top_p: 1.0
     top_k: null
+    val_temperature: ${.temperature}
+    val_top_p: ${.top_p}
+    val_top_k: ${.top_k}
     stop_token_ids: null
     stop_strings: null
     mcore_generation_config:
diff --git a/tests/unit/utils/test_native_checkpoint.py b/tests/unit/utils/test_native_checkpoint.py
index 33240d92888..ba7b1151d64 100755
--- a/tests/unit/utils/test_native_checkpoint.py
+++ b/tests/unit/utils/test_native_checkpoint.py
@@ -73,6 +73,9 @@
         "temperature": 1.0,
         "top_p": 1.0,
         "top_k": None,
+        "val_temperature": 1.0,
+        "val_top_p": 1.0,
+        "val_top_k": None,
         "backend": "vllm",
         "colocated": {"enabled": True},
     },