diff --git a/.gitignore b/.gitignore
index 5d5611d1c2..13f5629fcb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,3 +46,7 @@ code_snapshots*/
 # Runtime env
 *runtime_env.yaml
 !default_runtime_env.yaml
+
+# Local environment variables
+.env
+.envrc
diff --git a/.gitmodules b/.gitmodules
index 7c2bda3560..c5b71c13f7 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,6 +1,6 @@
 [submodule "3rdparty/Megatron-LM"]
 	path = 3rdparty/Megatron-LM-workspace/Megatron-LM
-	url = https://github.com/terrykong/Megatron-LM.git
+	url = https://github.com/ahmadki/Megatron-LM.git
 	branch = yuya/nemo-rl-use-dev
 	shallow = true
 [submodule "3rdparty/Megatron-Bridge"]
diff --git a/3rdparty/Automodel-workspace/Automodel b/3rdparty/Automodel-workspace/Automodel
index a2db048383..4546696277 160000
--- a/3rdparty/Automodel-workspace/Automodel
+++ b/3rdparty/Automodel-workspace/Automodel
@@ -1 +1 @@
-Subproject commit a2db048383cd54b3fafc928df4c30bf7bbf7c430
+Subproject commit 45466962771e9aac6f6f26eb05a0b287983a9b33
diff --git a/3rdparty/Megatron-Bridge-workspace/Megatron-Bridge b/3rdparty/Megatron-Bridge-workspace/Megatron-Bridge
index f003cd8ca3..b6728896fd 160000
--- a/3rdparty/Megatron-Bridge-workspace/Megatron-Bridge
+++ b/3rdparty/Megatron-Bridge-workspace/Megatron-Bridge
@@ -1 +1 @@
-Subproject commit f003cd8ca3e4876853b6097e816f0a94ea8fefc1
+Subproject commit b6728896fd2215da024981169f30baaee1e70e56
diff --git a/3rdparty/Megatron-Bridge-workspace/setup.py b/3rdparty/Megatron-Bridge-workspace/setup.py
index 375e280411..5809ee6312 100644
--- a/3rdparty/Megatron-Bridge-workspace/setup.py
+++ b/3rdparty/Megatron-Bridge-workspace/setup.py
@@ -37,11 +37,12 @@
     "pyyaml>=6.0.2",
     "tqdm>=4.67.1",
     "hydra-core>1.3,<=1.3.2",
-    "megatron-core[dev,mlm]>=0.15.0a0,<0.16.0",
+    "megatron-core[dev,mlm]>=0.15.0a0,<0.17.0",
     "qwen-vl-utils",
     "transformer-engine[pytorch]>=2.9.0a0,<2.10.0",
     "mamba-ssm",
     "nvidia-resiliency-ext",
+    "transformers>=4.57.1",
     "causal-conv1d",
 ]
 
diff --git a/3rdparty/Megatron-LM-workspace/Megatron-LM b/3rdparty/Megatron-LM-workspace/Megatron-LM
index 76065f17e1..0d401602bf 160000
--- a/3rdparty/Megatron-LM-workspace/Megatron-LM
+++ b/3rdparty/Megatron-LM-workspace/Megatron-LM
@@ -1 +1 @@
-Subproject commit 76065f17e1e1e2850d1e9009ae5f601007aeeeb3
+Subproject commit 0d401602bf48046683adfc2542a70613f6e772e6
diff --git a/examples/configs/grpo_math_1B.yaml b/examples/configs/grpo_math_1B.yaml
index 50124bb71e..303a375f54 100644
--- a/examples/configs/grpo_math_1B.yaml
+++ b/examples/configs/grpo_math_1B.yaml
@@ -157,6 +157,7 @@ policy:
       overlap_param_gather: true
       use_custom_fsdp: false
       data_parallel_sharding_strategy: "optim_grads_params"
+      average_in_collective: true
 
     fp8_cfg: null
 
diff --git a/examples/configs/recipes/llm/grpo-deepscaler-1.5b-16K.yaml b/examples/configs/recipes/llm/grpo-deepscaler-1.5b-16K.yaml
index ccfa867209..1a5af7ed40 100644
--- a/examples/configs/recipes/llm/grpo-deepscaler-1.5b-16K.yaml
+++ b/examples/configs/recipes/llm/grpo-deepscaler-1.5b-16K.yaml
@@ -1,6 +1,6 @@
 defaults:
-  - ../../grpo_math_1B.yaml
-  - grpo-deepscaler-1.5b-8K.yaml
+- ../../grpo_math_1B.yaml
+- grpo-deepscaler-1.5b-8K.yaml
 loss_fn:
   reference_policy_kl_penalty: 0.001
   ratio_clip_max: 0.28
diff --git a/examples/configs/recipes/llm/grpo-deepscaler-1.5b-24K.yaml b/examples/configs/recipes/llm/grpo-deepscaler-1.5b-24K.yaml
index ccdb67197b..7685eddb59 100644
--- a/examples/configs/recipes/llm/grpo-deepscaler-1.5b-24K.yaml
+++ b/examples/configs/recipes/llm/grpo-deepscaler-1.5b-24K.yaml
@@ -1,6 +1,6 @@
 defaults:
-  - ../../grpo_math_1B.yaml
-  - grpo-deepscaler-1.5b-8K.yaml
+- ../../grpo_math_1B.yaml
+- grpo-deepscaler-1.5b-8K.yaml
 loss_fn:
   reference_policy_kl_penalty: 0.0001
   ratio_clip_max: 0.28
diff --git a/examples/configs/recipes/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.yaml b/examples/configs/recipes/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.yaml
new file mode 100644
index 0000000000..2293ff17e3
--- /dev/null
+++ b/examples/configs/recipes/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.yaml
@@ -0,0 +1,47 @@
+defaults: ../../grpo_math_1B.yaml
+grpo:
+  num_prompts_per_step: 64
+  num_generations_per_prompt: 32
+checkpointing:
+  checkpoint_dir: results/grpo-qwen3-next-80ba3b-8n8g-megatron
+policy:
+  model_name: Qwen/Qwen3-Next-80B-A3B-Instruct
+  train_micro_batch_size: 1
+  max_total_sequence_length: 4096
+  dtensor_cfg:
+    enabled: false
+  optimizer: null
+  scheduler: null
+  sequence_packing:
+    enabled: false
+    algorithm: modified_ffd
+  make_sequence_length_divisible_by: ${policy.megatron_cfg.tensor_model_parallel_size}
+  megatron_cfg:
+    enabled: true
+    converter_type: Qwen3NextForCausalLM
+    tensor_model_parallel_size: 2
+    pipeline_model_parallel_size: 4
+    expert_model_parallel_size: 4
+    sequence_parallel: true
+    optimizer:
+      lr: 3.0e-07
+      min_lr: 3.0e-08
+    scheduler:
+      lr_warmup_iters: 50
+      lr_warmup_init: 3.0e-08
+    env_vars:
+      PYTORCH_CUDA_ALLOC_CONF: expandable_segments:False
+  generation:
+    vllm_cfg:
+      tensor_parallel_size: 4
+      gpu_memory_utilization: 0.7
+logger:
+  log_dir: logs/grpo-qwen3-next-80ba3b-8n8g-megatron
+  wandb_enabled: true
+  tensorboard_enabled: true
+  wandb:
+    project: nemo-rl
+    name: grpo-qwen3-next-80ba3b-8n8g-megatron
+cluster:
+  gpus_per_node: 8
+  num_nodes: 8
diff --git a/examples/configs/recipes/llm/sft-qwen3-next-80ba3b-8n8g-megatron.yaml b/examples/configs/recipes/llm/sft-qwen3-next-80ba3b-8n8g-megatron.yaml
new file mode 100644
index 0000000000..7d3ccc1348
--- /dev/null
+++ b/examples/configs/recipes/llm/sft-qwen3-next-80ba3b-8n8g-megatron.yaml
@@ -0,0 +1,47 @@
+defaults: ../../sft.yaml
+sft:
+  max_num_steps: 1000000
+  val_period: 50
+checkpointing:
+  checkpoint_dir: results/sft-qwen3-next-80ba3b-instruct-8n8g-megatron
+  save_period: 50
+policy:
+  model_name: Qwen/Qwen3-Next-80B-A3B-Instruct
+  tokenizer:
+    name: Qwen/Qwen3-Next-80B-A3B-Instruct
+    chat_template: default
+  train_global_batch_size: 512
+  max_total_sequence_length: 4096
+  dtensor_cfg:
+    enabled: false
+  make_sequence_length_divisible_by: ${policy.megatron_cfg.tensor_model_parallel_size}
+  optimizer: null
+  megatron_cfg:
+    enabled: true
+    converter_type: Qwen3NextForCausalLM
+    pipeline_model_parallel_size: 2
+    expert_model_parallel_size: 8
+    optimizer:
+      lr: 2.0e-05
+      min_lr: 1.99999e-05
+      weight_decay: 0.01
+      bf16: true
+    scheduler:
+      lr_warmup_init: 1.9999e-65
+data:
+  dataset_name: openmathinstruct2
+  prompt_file: examples/prompts/math.txt
+  split: train_1M
+  add_generation_prompt: true
+  output_key: generated_solution
+  seed: 42
+logger:
+  log_dir: logs/sft-qwen3-next-80ba3b-instruct-8n8g-megatron
+  wandb:
+    project: nemo-rl
+    name: sft-qwen3-next-80ba3b-instruct-8n8g-megatron
+  tensorboard:
+    log_dir: tb_logs-sft-dev-openmathinstruct2
+cluster:
+  num_nodes: 8
+  gpus_per_node: 8
diff --git a/examples/configs/sft.yaml b/examples/configs/sft.yaml
index 2482678374..85519b8839 100644
--- a/examples/configs/sft.yaml
+++ b/examples/configs/sft.yaml
@@ -15,7 +15,7 @@ sft:
 checkpointing:
   enabled: true
   checkpoint_dir: "results/sft"
-  metric_name: "val:val_loss" # one of "val:" or "train:" followed by the metric name
+  metric_name: "val:val_loss" ## set to null to save most recent k checkpoints
   higher_is_better: false
   keep_top_k: 3
   save_period: 10
@@ -37,7 +37,6 @@ policy:
 
   dtensor_cfg:
     enabled: true
-    env_vars: {}
     cpu_offload: False
     sequence_parallel: false
     activation_checkpointing: false
@@ -76,7 +75,6 @@ policy:
   ## ignored since enabled=false, but needed for testing purposes
   megatron_cfg:
     enabled: false
-    env_vars: {}
     empty_unused_memory_level: 1
     activation_checkpointing: false
     tensor_model_parallel_size: 1
@@ -97,7 +95,7 @@ policy:
     apply_rope_fusion: True
     # gives ~25% training perf speedup with sequence packing and apply_rope_fusion
     bias_activation_fusion: True
-    defer_fp32_logits: False
+    defer_fp32_logits: null
 
     optimizer:
       optimizer: "adam"
@@ -139,6 +137,7 @@ policy:
       grad_reduce_in_fp32: false
       overlap_grad_reduce: true
       overlap_param_gather: true
+      average_in_collective: true
       data_parallel_sharding_strategy: "optim_grads_params"
       use_custom_fsdp: false
 
diff --git a/examples/configs/vlm_grpo_3B.yaml b/examples/configs/vlm_grpo_3B.yaml
index 4e21205491..e85d160c68 100644
--- a/examples/configs/vlm_grpo_3B.yaml
+++ b/examples/configs/vlm_grpo_3B.yaml
@@ -33,11 +33,6 @@ grpo:
 
 loss_fn:
   reference_policy_kl_penalty: 0.01
-  # Can be set to k1, k2, k3
-  # For more details, see http://joschu.net/blog/kl-approx.html
-  reference_policy_kl_type: "k3"
-  kl_input_clamp_value: 20.0
-  kl_output_clamp_value: 10.0
   ratio_clip_min: 0.2
   ratio_clip_max: 0.2
   ratio_clip_c: null
@@ -50,7 +45,7 @@ loss_fn:
 checkpointing:
   enabled: true
   checkpoint_dir: "results/clevr_grpo_${policy.model_name}"
-  metric_name: "val:accuracy" # one of "val:" or "train:" followed by the metric name
+  metric_name: "val_reward"
   higher_is_better: true
   keep_top_k: 3
   save_period: 10
@@ -101,7 +96,7 @@ policy:
     apply_rope_fusion: True
     # gives ~25% training perf speedup with sequence packing and apply_rope_fusion
     bias_activation_fusion: True
-    defer_fp32_logits: False
+    defer_fp32_logits: null
 
     optimizer:
       optimizer: "adam"
@@ -143,6 +138,7 @@ policy:
       grad_reduce_in_fp32: false
       overlap_grad_reduce: true
       overlap_param_gather: true
+      average_in_collective: true
       use_custom_fsdp: false
       data_parallel_sharding_strategy: "optim_grads_params"
 
diff --git a/examples/configs/vlm_grpo_3B_megatron.yaml b/examples/configs/vlm_grpo_3B_megatron.yaml
index dd206d75ac..49cd912053 100644
--- a/examples/configs/vlm_grpo_3B_megatron.yaml
+++ b/examples/configs/vlm_grpo_3B_megatron.yaml
@@ -30,11 +30,6 @@ grpo:
     max_trajectory_age_steps: 1
 loss_fn:
   reference_policy_kl_penalty: 0.01
-  # Can be set to k1, k2, k3
-  # For more details, see http://joschu.net/blog/kl-approx.html
-  reference_policy_kl_type: "k3"
-  kl_input_clamp_value: 20.0
-  kl_output_clamp_value: 10.0
   ratio_clip_min: 0.2
   ratio_clip_max: 0.2
   ratio_clip_c: null
@@ -45,7 +40,7 @@ loss_fn:
 checkpointing:
   enabled: true
   checkpoint_dir: results/clevr_grpo_${policy.model_name}
-  metric_name: val:accuracy # one of "val:" or "train:" followed by the metric name
+  metric_name: val_reward
   higher_is_better: true
   keep_top_k: 3
   save_period: 10
@@ -83,6 +78,7 @@ policy:
     logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}}
     algorithm: modified_first_fit_decreasing
     sequence_length_round: 64
+  optimizer: null
   scheduler:
   - name: torch.optim.lr_scheduler.LinearLR
     kwargs:
@@ -142,7 +138,7 @@ policy:
     apply_rope_fusion: true
     # gives ~25% training perf speedup with sequence packing and apply_rope_fusion
     bias_activation_fusion: True
-    defer_fp32_logits: False
+    defer_fp32_logits: null
     optimizer:
       optimizer: adam
       lr: 2.0e-07
@@ -173,6 +169,7 @@ policy:
       grad_reduce_in_fp32: false
       overlap_grad_reduce: false
       overlap_param_gather: true
+      average_in_collective: true
       use_custom_fsdp: false
       data_parallel_sharding_strategy: optim_grads_params
 data:
diff --git a/nemo_rl/models/policy/__init__.py b/nemo_rl/models/policy/__init__.py
index bf8599a595..fa988ae1bd 100644
--- a/nemo_rl/models/policy/__init__.py
+++ b/nemo_rl/models/policy/__init__.py
@@ -12,34 +12,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Literal, NotRequired, TypedDict, Union
+from typing import Any, NotRequired, TypedDict, Union
 
 from nemo_rl.models.generation.interfaces import GenerationConfig
 
 
-class DTensorConfigDisabled(TypedDict):
-    enabled: Literal[False]
-
-
 class DTensorConfig(TypedDict):
-    enabled: Literal[True]
-    env_vars: NotRequired[dict[str, str] | None]
+    enabled: bool
+    env_vars: NotRequired[dict[str, str]]
     _v2: NotRequired[bool]
-    cpu_offload: bool
-    sequence_parallel: bool
-    activation_checkpointing: bool
-    tensor_parallel_size: int
-    context_parallel_size: int
-    custom_parallel_plan: str | None
-    clear_cache_every_n_steps: NotRequired[int | None]
-
-
-class SequencePackingConfigDisabled(TypedDict):
-    enabled: Literal[False]
+    cpu_offload: NotRequired[bool]
+    sequence_parallel: NotRequired[bool]
+    activation_checkpointing: NotRequired[bool]
+    tensor_parallel_size: NotRequired[int]
+    context_parallel_size: NotRequired[int]
+    custom_parallel_plan: NotRequired[str]
+    clear_cache_every_n_steps: NotRequired[int]
 
 
 class SequencePackingConfig(TypedDict):
-    enabled: Literal[True]
+    enabled: bool
     train_mb_tokens: int
     # Not required because some algorithms like SFT don't calculate log probs
     logprob_mb_tokens: NotRequired[int]
@@ -81,7 +73,7 @@ class MegatronSchedulerConfig(TypedDict):
     end_weight_decay: float
     weight_decay_incr_style: str
     lr_decay_style: str
-    lr_decay_iters: NotRequired[int | None]
+    lr_decay_iters: int
     lr_warmup_iters: int
     lr_warmup_init: float
 
@@ -90,34 +82,29 @@ class MegatronDDPConfig(TypedDict):
     grad_reduce_in_fp32: bool
     overlap_grad_reduce: bool
     overlap_param_gather: bool
+    average_in_collective: bool
     use_custom_fsdp: bool
     data_parallel_sharding_strategy: str
 
 
-# Type exists to be lax if not specified
-class MegatronConfigDisabled(TypedDict):
-    enabled: Literal[False]
-
-
 class MegatronConfig(TypedDict):
-    enabled: Literal[True]
-    env_vars: NotRequired[dict[str, str] | None]
+    enabled: bool
+    env_vars: NotRequired[dict[str, str]]
     # 1 is the minimum recommendation for RL since we almost always need to offload before beginning generation.
     # Setting to 0 is faster, but you are more likely to run out of GPU memory. In SFT/DPO, the default is 0.
     empty_unused_memory_level: int
     activation_checkpointing: bool
+    converter_type: str
     tensor_model_parallel_size: int
     pipeline_model_parallel_size: int
-    num_layers_in_first_pipeline_stage: int | None
-    num_layers_in_last_pipeline_stage: int | None
+    num_layers_in_first_pipeline_stage: int
+    num_layers_in_last_pipeline_stage: int
     context_parallel_size: int
     pipeline_dtype: str
     sequence_parallel: bool
     freeze_moe_router: bool
     expert_tensor_parallel_size: int
     expert_model_parallel_size: int
-    # If True, defer the casting of logits to float32 until the backward pass.
-    # If you are using logprob_chunk_size, you must set this to True.
     defer_fp32_logits: NotRequired[bool]
     # gives ~20% training perf speedup with sequence packing
     apply_rope_fusion: bool
@@ -126,8 +113,8 @@ class MegatronConfig(TypedDict):
     # Force overwrite of the initial checkpoint even if it exists (default: False)
     force_overwrite_initial_ckpt: NotRequired[bool]
 
-    optimizer: MegatronOptimizerConfig
-    scheduler: MegatronSchedulerConfig
+    optimizer: NotRequired[MegatronOptimizerConfig]
+    scheduler: NotRequired[MegatronSchedulerConfig]
     distributed_data_parallel_config: MegatronDDPConfig
 
 
@@ -135,7 +122,7 @@ class TokenizerConfig(TypedDict):
     name: str
     chat_template: NotRequired[str]
     # Arguments to pass to tokenizer.apply_chat_template(...). This can be used to pass kwargs like enable_thinking=true
-    chat_template_kwargs: NotRequired[dict[str, Any] | None]
+    chat_template_kwargs: NotRequired[dict[str, Any]]
 
 
 class PytorchOptimizerConfig(TypedDict):
@@ -146,29 +133,24 @@ class PytorchOptimizerConfig(TypedDict):
 class SinglePytorchSchedulerConfig(TypedDict):
     name: str
     kwargs: dict[str, Any]
-
-
-class SinglePytorchMilestonesConfig(TypedDict):
-    milestones: list[int]  # Used in SequentialLR configuration
+    milestones: NotRequired[list[int]]  # Used in SequentialLR configuration
 
 
 SchedulerMilestones = dict[str, list[int]]
 
 
-class DynamicBatchingConfigDisabled(TypedDict):
-    enabled: Literal[False]
-
-
 class DynamicBatchingConfig(TypedDict):
     # dynamic_batching improves performance by ensuring logprob and training microbatches
     # have a sufficent number of tokens to maximize GPU utilization. Specifically, variable length
     # responses are sorted by sequence length and bucketed into microbatches with a total
     # amount of tokens is approximately close to 'train_mb_tokens' and 'logprob_mb_tokens' for the
     # training and logprob stages respectively.
-    enabled: Literal[True]
-    train_mb_tokens: int
-    logprob_mb_tokens: NotRequired[int]  # Only used for some algorithms
-    sequence_length_round: int
+    enabled: bool
+
+    ## required if enabled is true
+    train_mb_tokens: NotRequired[int]
+    logprob_mb_tokens: NotRequired[int]
+    sequence_length_round: NotRequired[int]
 
 
 class PolicyConfig(TypedDict):
@@ -177,29 +159,21 @@ class PolicyConfig(TypedDict):
     train_global_batch_size: int
     train_micro_batch_size: int
     logprob_batch_size: NotRequired[int]
-    # If set, log probability computation is chunked along the sequence dimension to avoid GPU OOM (especially during backward pass).
-    # Within each chunk loop, logits casting (from float16/bfloat16 to float32) is done to prevent holding the entire float32 logits tensor in memory.
-    # If None, chunking is disabled and the full sequence is processed at once.
-    logprob_chunk_size: NotRequired[int | None]
+    logprob_chunk_size: NotRequired[int]
     generation: NotRequired[GenerationConfig]
     generation_batch_size: NotRequired[
         int
     ]  # used in static batched (framework) generation
     precision: str
     reward_model_cfg: NotRequired[RewardModelConfig]
-    dtensor_cfg: DTensorConfig | DTensorConfigDisabled
-    megatron_cfg: NotRequired[MegatronConfig | MegatronConfigDisabled]
+    dtensor_cfg: DTensorConfig
+    megatron_cfg: NotRequired[MegatronConfig]
     hf_config_overrides: NotRequired[dict[str, Any]]
-    dynamic_batching: DynamicBatchingConfig | DynamicBatchingConfigDisabled
-    sequence_packing: NotRequired[SequencePackingConfig | SequencePackingConfigDisabled]
+    dynamic_batching: DynamicBatchingConfig
+    sequence_packing: NotRequired[SequencePackingConfig]
     make_sequence_length_divisible_by: int
     max_total_sequence_length: int
-    # This sets the clipping norm for the DTensorPolicyWorkers (Megatron's is called clip_grad)
-    max_grad_norm: NotRequired[float | int | None]
+    max_grad_norm: NotRequired[Union[float, int]]
     refit_buffer_size_gb: NotRequired[float]
-    optimizer: NotRequired[PytorchOptimizerConfig | None]
-    scheduler: NotRequired[
-        list[SinglePytorchSchedulerConfig | SinglePytorchMilestonesConfig]
-        | SchedulerMilestones
-        | None
-    ]
+    optimizer: NotRequired[PytorchOptimizerConfig]
+    scheduler: NotRequired[list[SinglePytorchSchedulerConfig] | SchedulerMilestones]
diff --git a/nemo_rl/models/policy/megatron_policy_worker.py b/nemo_rl/models/policy/megatron_policy_worker.py
index 3bf211be5c..2f1e6c3a48 100644
--- a/nemo_rl/models/policy/megatron_policy_worker.py
+++ b/nemo_rl/models/policy/megatron_policy_worker.py
@@ -642,14 +642,6 @@ def __init__(
             assert optimizer_offload_fraction == 1.0, (
                 "Currently for optimizer offloading, only optimizer_offload_fraction=1.0 is supported"
             )
-        if (
-            "logprob_chunk_size" in self.cfg
-            and self.cfg["logprob_chunk_size"] is not None
-            and self.cfg["logprob_chunk_size"] > 0
-        ):
-            assert self.cfg["megatron_cfg"]["defer_fp32_logits"], (
-                "defer_fp32_logits must be True if logprob_chunk_size is set"
-            )
 
         checkpoint_config = CheckpointConfig(
             save_interval=100,
@@ -674,22 +666,6 @@ def __init__(
             "https://github.com/NVIDIA-NeMo/RL/blob/bccbc377705a81a1f4b3c31ad9767bcc15f735a8/nemo_rl/algorithms/sft.py#L175-L179."
         )
 
-        ## These settings are required for correct gradient computations in mcore
-        ## when calculate_per_token_loss is True, there is no scaling of the gradient in mcore,
-        ## so we handle the scaling in nemo-rl.
-        ## perform_initialization = True is a workaround to ensure the correct tensor parallel attributes are set
-        ## on the TP-sharded parameters.
-        model_cfg.calculate_per_token_loss = True
-        model_cfg.perform_initialization = True
-
-        assert (
-            "aux_loss" not in model_cfg.moe_router_load_balancing_type
-            or model_cfg.moe_aux_loss_coeff == 0
-        ), (
-            "MoE aux loss is currently not supported due to a known bug in Megatron-LM. "
-            "See https://github.com/NVIDIA/Megatron-LM/issues/1984 for more details."
-        )
-
         self.megatron_cfg = ConfigContainer(
             model=model_cfg,
             checkpoint=checkpoint_config,
@@ -715,9 +691,9 @@ def __init__(
                 overlap_param_gather=self.cfg["megatron_cfg"][
                     "distributed_data_parallel_config"
                 ]["overlap_param_gather"],
-                # we need to set average_in_collective=False with calculate_per_token_loss=True.
-                # otherwise, mcore throws an assertion error.
-                average_in_collective=False,
+                average_in_collective=self.cfg["megatron_cfg"][
+                    "distributed_data_parallel_config"
+                ]["average_in_collective"],
                 use_distributed_optimizer=self.cfg["megatron_cfg"]["optimizer"][
                     "use_distributed_optimizer"
                 ],
@@ -1114,6 +1090,7 @@ def train(
                         curr_wd = self.scheduler.get_wd()
                         loss_metrics["lr"] = curr_lr
                         loss_metrics["wd"] = curr_wd
+                        loss_metrics["grad_norm"] = grad_norm
                         loss_metrics["global_valid_seqs"] = global_valid_seqs.item()
                         loss_metrics["global_valid_toks"] = global_valid_toks.item()
                         mb_losses.append(loss_metrics["loss"])
@@ -1162,7 +1139,9 @@ def train(
             "gpu_name": torch.cuda.get_device_name(),
             "model_dtype": self.dtype,
             "all_mb_metrics": dict(mb_metrics),
-            "grad_norm": torch.tensor([grad_norm]),
+            "grad_norm": torch.tensor(
+                mb_metrics["grad_norm"][-1]
+            ).cpu(),  # TODO @sahilj: return an average or something later
         }
         return metrics
 
@@ -2293,50 +2272,6 @@ def report_node_ip_and_gpu_id(self) -> list[tuple[str, int]]:
         gpu_id = ray.get_gpu_ids()[0]
         return (ip, gpu_id)
 
-    def check_tensor_parallel_attributes(self) -> dict[str, Any]:
-        """Check tensor parallel attributes on model parameters.
-
-        Returns:
-            Dictionary containing information about tensor parallel parameters:
-            - tp_params: List of parameter names that have tensor_model_parallel=True
-            - non_tp_params: List of parameter names that have tensor_model_parallel=False
-            - total_params: Total number of parameters checked
-            - tp_size: Tensor parallel size from config
-        """
-        tp_params = []
-        non_tp_params = []
-        total_params = 0
-
-        for name, param in self.model.named_parameters():
-            total_params += 1
-            tensor_model_parallel = getattr(param, "tensor_model_parallel", False)
-
-            if tensor_model_parallel:
-                tp_params.append(
-                    {
-                        "name": name,
-                        "tensor_model_parallel": tensor_model_parallel,
-                        "partition_dim": getattr(param, "partition_dim", None),
-                        "partition_stride": getattr(param, "partition_stride", None),
-                        "shape": list(param.shape),
-                    }
-                )
-            else:
-                non_tp_params.append(
-                    {
-                        "name": name,
-                        "tensor_model_parallel": tensor_model_parallel,
-                        "shape": list(param.shape),
-                    }
-                )
-
-        return {
-            "tp_params": tp_params,
-            "non_tp_params": non_tp_params,
-            "total_params": total_params,
-            "tp_size": self.megatron_cfg.model.tensor_model_parallel_size,
-        }
-
 
 class CustomFloat16Module(Float16Module):
     """Float 16 Module.
diff --git a/nemo_rl/models/policy/utils.py b/nemo_rl/models/policy/utils.py
index 0d12c60ecd..5227351dc3 100644
--- a/nemo_rl/models/policy/utils.py
+++ b/nemo_rl/models/policy/utils.py
@@ -292,7 +292,7 @@ def sliding_window_overwrite(model_name: str) -> dict[str, Any]:
         hasattr(hf_config, "use_sliding_window")
         and hf_config.use_sliding_window == False
     ):
-        assert hasattr(hf_config, "sliding_window")
+        # assert hasattr(hf_config, "sliding_window")
         overwrite_dict = {
             "sliding_window": None,
         }
diff --git a/pyproject.toml b/pyproject.toml
index 56491b10a3..72d3870ccb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,12 +20,12 @@ requires-python = ">=3.12"
 license = {text = "Apache 2.0"}
 dependencies = [
     "setuptools",
-    "ninja",  # for flash-attn parallel build
+    "ninja", # for flash-attn parallel build
     "torch==2.8.0",
     "triton",
     "colored==2.2.3",
     "ray[default]==2.49.2",
-    "transformers>=4.55.4",
+    "transformers==4.57.1",
     "wandb",
     "numpy",
     "datasets>=4.0.0",
@@ -46,11 +46,12 @@ dependencies = [
     "sympy>=1.14.0",
     "pillow>=11.3.0",
     "torchvision>=0.22.0",
-    "num2words>=0.5.14",   # for SmolVLM
-    "mlflow>=3.5.0,<3.6.0",
+    "num2words>=0.5.14", # for SmolVLM
+    "mlflow",
     "nvidia-nvshmem-cu12", # for deep_ep build
     "swanlab",
     "pyzmq",
+    "rwkv-fla>=0.7.202508221413",
 ]
 
 [project.optional-dependencies]
@@ -100,6 +101,7 @@ mcore = [
     # https://github.com/NVIDIA/TransformerEngine/blob/v2.3/transformer_engine/pytorch/attention/dot_product_attention/utils.py#L108
     # https://github.com/facebookresearch/xformers/blob/8354497deb2c04c67fbb2e2ad911e86530da0e90/xformers/ops/fmha/flash.py#L76
     "flash-attn==2.8.1",
+    "rwkv-fla>=0.7.202508221413",
 ]
 penguin = [
     "penguin",
@@ -299,7 +301,7 @@ ignore = ["D417", "D10", "F841"]
 convention = "google"
 
 # Section to exclude errors for different file types
-[tool.ruff.lint.per-file-ignores]
+[tool.ruff.per-file-ignores]
 # Ignore all directories named `tests`.
 "tests/**" = ["D"]
 # Ignore all files that end in `_test.py`.
diff --git a/tests/test_suites/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.sh b/tests/test_suites/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.sh
new file mode 100755
index 0000000000..cfb9562298
--- /dev/null
+++ b/tests/test_suites/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)
+source $SCRIPT_DIR/common.env
+
+# ===== BEGIN CONFIG =====
+NUM_NODES=8
+STEPS_PER_RUN=30
+MAX_STEPS=30
+NUM_RUNS=$(( (MAX_STEPS + STEPS_PER_RUN - 1) / STEPS_PER_RUN ))  # Round up
+NUM_MINUTES=240
+# ===== END CONFIG =====
+
+exit_if_max_steps_reached
+
+# Run the experiment
+cd $PROJECT_ROOT
+uv run examples/run_grpo_math.py \
+    --config $CONFIG_PATH \
+    grpo.max_num_steps=$MAX_STEPS \
+    logger.log_dir=$LOG_DIR \
+    logger.wandb_enabled=True \
+    logger.wandb.project=nemo-rl \
+    logger.wandb.name=$EXP_NAME \
+    logger.monitor_gpus=True \
+    logger.tensorboard_enabled=True \
+    checkpointing.enabled=True \
+    checkpointing.checkpoint_dir=$CKPT_DIR \
+    $@ \
+    2>&1 | tee $RUN_LOG
+
+# Convert tensorboard logs to json
+uv run tests/json_dump_tb_logs.py $LOG_DIR --output_path $JSON_METRICS
+
+# Only run metrics if the target step is reached
+if [[ $(jq 'to_entries | .[] | select(.key == "train/loss") | .value | keys | map(tonumber) | max' $JSON_METRICS) -ge $MAX_STEPS ]]; then
+    uv run tests/check_metrics.py $JSON_METRICS \
+        'data["train/token_mult_prob_error"]["30"] < 1.1' \
+        'data["train/reward"]["30"] > 0.43' \
+        'mean(data["timing/train/total_step_time"], -6, -1) < 305'
+fi
diff --git a/tests/test_suites/llm/sft-qwen3-next-80ba3b-8n8g-megatron.sh b/tests/test_suites/llm/sft-qwen3-next-80ba3b-8n8g-megatron.sh
new file mode 100755
index 0000000000..2c14b86292
--- /dev/null
+++ b/tests/test_suites/llm/sft-qwen3-next-80ba3b-8n8g-megatron.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)
+source $SCRIPT_DIR/common.env
+
+# ===== BEGIN CONFIG =====
+NUM_NODES=8
+STEPS_PER_RUN=200
+MAX_STEPS=200
+NUM_RUNS=$(( (MAX_STEPS + STEPS_PER_RUN - 1) / STEPS_PER_RUN ))  # Round up
+NUM_MINUTES=120
+# ===== END CONFIG =====
+
+exit_if_max_steps_reached
+
+# Run the experiment
+cd $PROJECT_ROOT
+uv run examples/run_sft.py \
+    --config $CONFIG_PATH \
+    sft.max_num_steps=$MAX_STEPS \
+    logger.log_dir=$LOG_DIR \
+    logger.wandb_enabled=True \
+    logger.wandb.project=nemo-rl \
+    logger.wandb.name=$EXP_NAME \
+    logger.monitor_gpus=True \
+    logger.tensorboard_enabled=True \
+    checkpointing.enabled=True \
+    checkpointing.checkpoint_dir=$CKPT_DIR \
+    $@ \
+    2>&1 | tee $RUN_LOG
+
+# Convert tensorboard logs to json
+uv run tests/json_dump_tb_logs.py $LOG_DIR --output_path $JSON_METRICS
+
+# Only run metrics if the target step is reached
+if [[ $(jq 'to_entries | .[] | select(.key == "train/loss") | .value | keys | map(tonumber) | max' $JSON_METRICS) -ge $MAX_STEPS ]]; then
+    uv run tests/check_metrics.py $JSON_METRICS \
+        'data["train/loss"]["200"] < 0.3' \
+        'data["validation/val_loss"]["200"] < 0.3' \
+        'mean(data["timing/train/total_step_time"], -6, -1) < 20'
+fi
diff --git a/tests/test_suites/nightly.txt b/tests/test_suites/nightly.txt
index 91c24aada9..aa066a29aa 100644
--- a/tests/test_suites/nightly.txt
+++ b/tests/test_suites/nightly.txt
@@ -48,6 +48,9 @@ tests/test_suites/llm/grpo-llama3.1-8b-instruct-2n8g-fsdp2tp1-noncolocated.sh
 #https://github.com/NVIDIA-NeMo/RL/issues/1374
 #tests/test_suites/llm/grpo-math-llama-nemotron-super-49b-v.5-4n8g-fsdp2tp8.sh
 
+# Qwen3-next
+tests/test_suites/llm/grpo-qwen3-next-80ba3b-8n8g-megatron.sh
+
 #######
 # SFT #
 #######
@@ -70,6 +73,9 @@ tests/test_suites/llm/sft-llama3.1-8b-1n8g-megatron-seqpack.sh
 # validate TP/DP
 tests/test_suites/llm/sft-qwen2.5-math7b-2n8g-megatron.sh
 
+# Qwen3-next
+tests/test_suites/llm/sft-qwen3-next-80ba3b-8n8g-megatron.sh
+
 #######
 # DPO #
 #######
diff --git a/uv.lock b/uv.lock
index aef07add6f..aabbb2c627 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,18 +2,15 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'",
-    "python_full_version >= '3.13' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "(python_full_version >= '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation != 'CPython' and sys_platform == 'linux')",
+    "(python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_python_implementation != 'CPython' and sys_platform == 'linux')",
+    "python_full_version == '3.14.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
     "python_full_version >= '3.13' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version < '3.13' and sys_platform == 'darwin'",
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
 ]
 
 [manifest]
@@ -64,7 +61,7 @@ wheels = [
 
 [[package]]
 name = "accelerate"
-version = "1.10.0"
+version = "1.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -76,9 +73,9 @@ dependencies = [
     { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
     { name = "torch", version = "2.8.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261, upload-time = "2025-08-07T10:54:51.664Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/23/60/2757c4f03a8705dbf80b1268b03881927878dca5ed07d74f733fb6c219e0/accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229", size = 393715, upload-time = "2025-10-20T14:42:25.025Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718, upload-time = "2025-08-07T10:54:49.988Z" },
+    { url = "https://files.pythonhosted.org/packages/77/85/85951bc0f9843e2c10baaa1b6657227056095de08f4d1eea7d8b423a6832/accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1", size = 375777, upload-time = "2025-10-20T14:42:23.256Z" },
 ]
 
 [[package]]
@@ -119,7 +116,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -130,42 +127,76 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716, upload-time = "2025-07-29T05:52:32.215Z" }
-wheels = [
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-    { url = "https://files.pythonhosted.org/packages/83/6d/0544e6b08b748682c30b9f65640d006e51f90763b41d7c546693bc22900d/aiohttp-3.12.15-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f2800614cd560287be05e33a679638e586a2d7401f4ddf99e304d98878c29444", size = 476948, upload-time = "2025-07-29T05:50:48.067Z" },
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 [[package]]
@@ -2450,11 +2756,11 @@ dependencies = [
 
 [[package]]
 name = "markdown"
-version = "3.8.2"
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@@ -3082,8 +3435,12 @@ source = { editable = "3rdparty/Automodel-workspace/Automodel" }
 dependencies = [
     { name = "bitsandbytes", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "datasets" },
+    { name = "diffusers" },
+    { name = "ftfy" },
+    { name = "imageio-ffmpeg" },
     { name = "liger-kernel", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "megatron-fsdp" },
+    { name = "opencv-python-headless" },
     { name = "pybind11" },
     { name = "pyyaml" },
     { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
@@ -3095,6 +3452,40 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
+all = [
+    { name = "backoff" },
+    { name = "flash-attn" },
+    { name = "mistral-common", extra = ["opencv"] },
+    { name = "numba" },
+    { name = "numpy" },
+    { name = "nvidia-nvshmem-cu13" },
+    { name = "pillow" },
+    { name = "qwen-vl-utils", extra = ["decord"], marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "timm" },
+    { name = "torchcodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "transformers" },
+]
+deepep = [
+    { name = "nvidia-nvshmem-cu13" },
+]
+dev = [
+    { name = "backoff" },
+    { name = "flash-attn" },
+    { name = "mistral-common", extra = ["opencv"] },
+    { name = "numba" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "qwen-vl-utils", extra = ["decord"], marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "timm" },
+    { name = "torchcodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "transformers" },
+]
 fa = [
     { name = "flash-attn" },
 ]
@@ -3107,9 +3498,12 @@ vlm = [
     { name = "numba" },
     { name = "numpy" },
     { name = "pillow" },
-    { name = "qwen-vl-utils", extra = ["decord"] },
+    { name = "qwen-vl-utils", extra = ["decord"], marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "timm" },
-    { name = "torchcodec" },
+    { name = "torchcodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "transformers" },
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@@ -3146,28 +3540,40 @@ requires-dist = [
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     { name = "bitsandbytes", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "==0.45.5" },
     { name = "datasets", specifier = ">=4.0.0" },
+    { name = "diffusers" },
     { name = "flash-attn", marker = "extra == 'fa'", specifier = "<=2.8.3" },
+    { name = "ftfy" },
+    { name = "imageio-ffmpeg" },
     { name = "liger-kernel", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = ">=0.5.9" },
     { name = "megatron-fsdp" },
     { name = "mistral-common", extras = ["opencv"], marker = "extra == 'vlm'" },
+    { name = "nemo-automodel", extras = ["deepep"], marker = "extra == 'all'", editable = "3rdparty/Automodel-workspace/Automodel" },
+    { name = "nemo-automodel", extras = ["fa"], marker = "extra == 'all'", editable = "3rdparty/Automodel-workspace/Automodel" },
+    { name = "nemo-automodel", extras = ["fa"], marker = "extra == 'dev'", editable = "3rdparty/Automodel-workspace/Automodel" },
+    { name = "nemo-automodel", extras = ["vlm"], marker = "extra == 'all'", editable = "3rdparty/Automodel-workspace/Automodel" },
+    { name = "nemo-automodel", extras = ["vlm"], marker = "extra == 'dev'", editable = "3rdparty/Automodel-workspace/Automodel" },
     { name = "numba", marker = "extra == 'vlm'" },
     { name = "numpy", marker = "extra == 'vlm'" },
+    { name = "nvidia-nvshmem-cu13", marker = "extra == 'deepep'" },
+    { name = "opencv-python-headless", specifier = "==4.10.0.84" },
     { name = "pillow", marker = "extra == 'vlm'" },
     { name = "pybind11" },
     { name = "pyyaml" },
-    { name = "qwen-vl-utils", extras = ["decord"], marker = "extra == 'vlm'" },
+    { name = "qwen-vl-utils", extras = ["decord"], marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'vlm'" },
     { name = "timm", marker = "extra == 'vlm'", specifier = "==1.0.16" },
     { name = "torch", marker = "sys_platform != 'darwin'", specifier = "<=2.8.0", index = "https://download.pytorch.org/whl/cu129" },
     { name = "torch", marker = "sys_platform == 'darwin'", specifier = "<=2.8.0", index = "https://pypi.org/simple" },
     { name = "torchao" },
-    { name = "torchcodec", marker = "extra == 'vlm'" },
+    { name = "torchcodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'vlm'" },
     { name = "torchdata" },
+    { name = "torchvision", marker = "sys_platform == 'darwin' and extra == 'vlm'", index = "https://pypi.org/simple" },
+    { name = "torchvision", marker = "sys_platform != 'darwin' and extra == 'vlm'", index = "https://download.pytorch.org/whl/cu129" },
     { name = "transformer-engine", extras = ["pytorch"], marker = "extra == 'moe'", specifier = "==2.8.0" },
-    { name = "transformers", specifier = "<=4.55.4" },
-    { name = "transformers", marker = "extra == 'vlm'", specifier = "<=4.55.4" },
+    { name = "transformers", specifier = "<=4.57.1" },
+    { name = "transformers", marker = "extra == 'vlm'", specifier = "<=4.57.1" },
     { name = "wandb" },
 ]
-provides-extras = ["vlm", "fa", "moe"]
+provides-extras = ["vlm", "fa", "moe", "deepep", "dev", "all"]
 
 [package.metadata.requires-dev]
 build = [
@@ -3220,6 +3626,7 @@ dependencies = [
     { name = "pyzmq" },
     { name = "ray", extra = ["default"] },
     { name = "rich" },
+    { name = "rwkv-fla" },
     { name = "setuptools" },
     { name = "swanlab" },
     { name = "sympy" },
@@ -3228,12 +3635,12 @@ dependencies = [
     { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
     { name = "torch", version = "2.8.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin'" },
     { name = "torchdata" },
-    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
     { name = "torchvision", version = "0.23.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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     { name = "triton", version = "3.4.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin'" },
-    { name = "triton", version = "3.4.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "triton", version = "3.5.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
     { name = "wandb" },
 ]
 
@@ -3249,6 +3656,7 @@ mcore = [
     { name = "flash-attn" },
     { name = "megatron-bridge" },
     { name = "megatron-core" },
+    { name = "rwkv-fla" },
     { name = "transformer-engine", extra = ["pytorch"] },
     { name = "vllm" },
 ]
@@ -3323,7 +3731,7 @@ requires-dist = [
     { name = "matplotlib" },
     { name = "megatron-bridge", marker = "extra == 'mcore'", editable = "3rdparty/Megatron-Bridge-workspace" },
     { name = "megatron-core", marker = "extra == 'mcore'", editable = "3rdparty/Megatron-LM-workspace" },
-    { name = "mlflow", specifier = ">=3.5.0,<3.6.0" },
+    { name = "mlflow" },
     { name = "nemo-automodel", marker = "extra == 'automodel'", editable = "3rdparty/Automodel-workspace/Automodel" },
     { name = "ninja" },
     { name = "num2words", specifier = ">=0.5.14" },
@@ -3339,6 +3747,8 @@ requires-dist = [
     { name = "pyzmq" },
     { name = "ray", extras = ["default"], specifier = "==2.49.2" },
     { name = "rich" },
+    { name = "rwkv-fla", specifier = ">=0.7.202508221413" },
+    { name = "rwkv-fla", marker = "extra == 'mcore'", specifier = ">=0.7.202508221413" },
     { name = "setuptools" },
     { name = "swanlab" },
     { name = "sympy", specifier = ">=1.14.0" },
@@ -3350,7 +3760,7 @@ requires-dist = [
     { name = "torchvision", marker = "sys_platform != 'darwin'", specifier = ">=0.22.0", index = "https://download.pytorch.org/whl/cu129" },
     { name = "torchvision", marker = "sys_platform == 'darwin'", specifier = ">=0.22.0", index = "https://pypi.org/simple" },
     { name = "transformer-engine", extras = ["pytorch"], marker = "extra == 'mcore'", specifier = "==2.8.0" },
-    { name = "transformers", specifier = ">=4.55.4" },
+    { name = "transformers", specifier = "==4.57.1" },
     { name = "triton", marker = "sys_platform != 'darwin'", index = "https://download.pytorch.org/whl/cu129" },
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     { name = "vllm", marker = "extra == 'automodel'", specifier = "==0.11.0" },
@@ -3432,7 +3842,7 @@ wheels = [
 
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+version = "3.9.2"
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@@ -3440,9 +3850,9 @@ dependencies = [
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