diff --git a/examples/configs/grpo_adk_llama8b.yaml b/examples/configs/grpo_adk_llama8b.yaml
new file mode 100644
index 0000000000..2c116fdfcf
--- /dev/null
+++ b/examples/configs/grpo_adk_llama8b.yaml
@@ -0,0 +1,44 @@
+# GRPO configuration for unique numbers environment
+defaults: "grpo_math_8B.yaml"
+
+grpo:
+  num_prompts_per_step: 32
+  num_generations_per_prompt: 16
+  max_rollout_turns: 20
+  max_num_steps: 100
+  val_at_start: false
+
+data:
+  add_system_prompt: false
+
+checkpointing:
+  enabled: false
+  checkpoint_dir: "results/grpo-adk"
+  metric_name: "val_reward"
+  higher_is_better: true
+  keep_top_k: 3
+  save_period: 10
+
+env:
+  unique_numbers:
+    cfg:
+      max_turns: 15
+      min_length: 5
+      max_length: 10
+      max_integer: 15
+
+logger:
+  wandb_enabled: True 
+  wandb:
+    project: "grpo-simulated-adk"
+    name: "llama-8b-__NOW__"
+
+policy:
+  train_global_batch_size: 512
+  dynamic_batching:
+    enabled: False
+  tokenizer:
+    chat_template: "{% for message in messages %}{% if loop.first %}<|begin_of_text|>{% endif %}<|start_header_id|>{{ message['role'] }}<|end_header_id|>\n{{ message['content'] }}<|eot_id|>{% endfor %}<|start_header_id|>assistant<|end_header_id|>\n"
+   
+cluster:
+  gpus_per_node: 8
diff --git a/examples/run_grpo_unique_numbers_w_adk.py b/examples/run_grpo_unique_numbers_w_adk.py
new file mode 100644
index 0000000000..c2401d35d4
--- /dev/null
+++ b/examples/run_grpo_unique_numbers_w_adk.py
@@ -0,0 +1,245 @@
+"""Run GRPO with the Unique Numbers Simulator using ADK.
+
+This script sets up and executes the Group Relative Policy Optimization (GRPO) algorithm
+in a multi-turn conversational environment powered by the ADK framework.
+
+### Task Overview
+The objective is to train an agent to guess the number of unique integers in a list generated by a simulated user.
+The interaction is structured as a turn-based dialogue:
+- The user generates a list of integers.
+- The agent queries specific positions in the list (by index).
+- The user replies with the value at that index (if available).
+- The agent continues the interaction until it makes a final guess at the number of unique integers.
+
+### Environment Details
+The environment is a simulated user that:
+- Randomly generates a list of integers at setup.
+- Responds to the agent's queries using an LLM via the ADK endpoint.
+- Optionally evaluates the agent's final guess using an LLM-based grader (included for extensibility, though not essential for this task).
+
+### Example Usage
+    uv run python examples/run_grpo_unique_numbers_w_adk.py
+
+### Requirements
+- A working ADK environment with access to a compatible LLM endpoint.
+  For the default Gemini endpoint, the following environment variables must be set:
+    - `GOOGLE_GENAI_USE_VERTEXAI=1`
+    - `GOOGLE_CLOUD_PROJECT="your-project-id"`
+    - `GOOGLE_CLOUD_LOCATION="your-location"`
+
+- A properly configured GRPO YAML file.
+  By default, the script uses:
+    `examples/configs/grpo_adk_llama8b.yaml`
+"""
+
+import argparse
+import itertools
+import os
+import pprint
+import random
+from datetime import datetime, timedelta
+from typing import Iterator
+
+from omegaconf import OmegaConf
+from torch.utils.data import IterableDataset
+from transformers import AutoTokenizer
+
+from nemo_rl.algorithms.grpo import MasterConfig, grpo_train, setup
+from nemo_rl.algorithms.utils import get_tokenizer
+from nemo_rl.data.interfaces import DatumSpec, LLMMessageLogType
+from nemo_rl.distributed.virtual_cluster import init_ray
+from nemo_rl.environments.simulated_user.prompt import starting_user_prompt
+from nemo_rl.environments.simulated_user.unique_numbers import (
+    UniqueNumbersEnv,
+    UniqueNumbersMetadata,
+)
+from nemo_rl.models.generation import configure_generation_config
+from nemo_rl.utils.config import load_config, parse_hydra_overrides
+from nemo_rl.utils.logger import get_next_experiment_dir
+
+OmegaConf.register_new_resolver("mul", lambda a, b: a * b)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run GRPO with unique numbers simulator"
+    )
+    parser.add_argument(
+        "--config", type=str, default=None, help="Path to YAML config file"
+    )
+    args, overrides = parser.parse_known_args()
+    return args, overrides
+
+
+def generate_datum(
+    tokenizer: AutoTokenizer,
+    env_cfg: dict,
+    task_name: str,
+    idx: int,
+    add_system_prompt: bool,
+) -> DatumSpec:
+    formatted_prompt = tokenizer.apply_chat_template(
+        [{"role": "user", "content": starting_user_prompt}],
+        tokenize=False,
+        add_system_prompt=add_system_prompt,
+        add_generation_prompt=True,
+        add_special_tokens=False,
+    ).strip()
+    token_ids = tokenizer(
+        formatted_prompt, return_tensors="pt", add_special_tokens=False
+    )["input_ids"][0]
+
+    def _generate_numbers(
+        min_length, max_length, max_integer, default_max_turns
+    ) -> UniqueNumbersMetadata:
+        length = random.randint(min_length, max_length)
+        numbers = [random.randint(0, max_integer) for _ in range(length)]
+        return UniqueNumbersMetadata(
+            numbers=numbers,
+            unique_count=len(set(numbers)),
+            turn=0,
+            max_turns=default_max_turns,
+        )
+
+    metadata = _generate_numbers(
+        min_length=env_cfg["cfg"]["min_length"],
+        max_length=env_cfg["cfg"]["max_length"],
+        max_integer=env_cfg["cfg"]["max_integer"],
+        default_max_turns=env_cfg["cfg"]["max_turns"],
+    )
+
+    message_log: LLMMessageLogType = [
+        {"role": "user", "content": formatted_prompt, "token_ids": token_ids}
+    ]
+    return {
+        "message_log": message_log,
+        "length": len(token_ids),
+        "extra_env_info": metadata,
+        "loss_multiplier": 1.0,
+        "idx": idx,
+        "task_name": task_name,
+    }
+
+
+class IterableNumbersDataset(IterableDataset):
+    def __init__(self, tokenizer, env_cfg, task_name, add_system_prompt, length):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.env_cfg = env_cfg
+        self.task_name = task_name
+        self.add_system_prompt = add_system_prompt
+        self.length = length
+
+    def __iter__(self) -> Iterator[DatumSpec]:
+        for i in itertools.count():
+            yield generate_datum(
+                tokenizer=self.tokenizer,
+                env_cfg=self.env_cfg,
+                task_name=self.task_name,
+                idx=i,
+                add_system_prompt=self.add_system_prompt,
+            )
+
+    def __len__(self):
+        return self.length
+
+
+def setup_data(tokenizer, env_cfg, task_name, length, val_length, add_system_prompt):
+    env_config = env_cfg[task_name]
+    env = UniqueNumbersEnv.options(num_gpus=0).remote(cfg=dict(env_config["cfg"]))
+    task_to_env = {task_name: env}
+
+    train_ds = IterableNumbersDataset(
+        tokenizer=tokenizer,
+        env_cfg=env_config,
+        task_name=task_name,
+        add_system_prompt=add_system_prompt,
+        length=length,
+    )
+    val_ds = IterableNumbersDataset(
+        tokenizer=tokenizer,
+        env_cfg=env_config,
+        task_name=task_name,
+        add_system_prompt=add_system_prompt,
+        length=val_length,
+    )
+    val_task_to_env = task_to_env
+    return train_ds, val_ds, task_to_env, val_task_to_env
+
+
+def main():
+    args, overrides = parse_args()
+    if not args.config:
+        args.config = os.path.join(
+            os.path.dirname(__file__), "configs", "grpo_adk_llama8b.yaml"
+        )
+    config = load_config(args.config)
+    if overrides:
+        config = parse_hydra_overrides(config, overrides)
+    config: MasterConfig = OmegaConf.to_container(config, resolve=True)
+
+    now_pst = datetime.utcnow() + timedelta(hours=-7)
+    config["logger"]["wandb"]["name"] = config["logger"]["wandb"]["name"].replace(
+        "__NOW__", now_pst.strftime("%m/%d-%H:%M")
+    )
+
+    config["logger"]["log_dir"] = get_next_experiment_dir(config["logger"]["log_dir"])
+    if config["checkpointing"]["enabled"]:
+        print(
+            f"\U0001f4ca Using checkpoint directory: {config['checkpointing']['checkpoint_dir']}"
+        )
+
+    pprint.pprint(config)
+
+    init_ray()
+
+    tokenizer = get_tokenizer(config["policy"]["tokenizer"])
+    config["policy"]["generation"] = configure_generation_config(
+        config["policy"]["generation"], tokenizer
+    )
+
+    ds_length = (
+        config["grpo"]["num_prompts_per_step"]
+        * config["grpo"]["num_generations_per_prompt"]
+        * config["grpo"]["max_num_steps"]
+    )
+    dataset, val_dataset, task_to_env, val_task_to_env = setup_data(
+        tokenizer=tokenizer,
+        env_cfg=config["env"],
+        task_name="unique_numbers",
+        length=ds_length,
+        val_length=config["grpo"]["max_val_samples"],
+        add_system_prompt=config["data"]["add_system_prompt"],
+    )
+
+    (
+        policy,
+        policy_generation,
+        cluster,
+        dataloader,
+        val_dataloader,
+        loss_fn,
+        logger,
+        checkpointer,
+        grpo_state,
+        master_config,
+    ) = setup(config, tokenizer, dataset, val_dataset)
+
+    grpo_train(
+        policy,
+        policy_generation,
+        dataloader,
+        val_dataloader,
+        tokenizer,
+        loss_fn,
+        task_to_env,
+        val_task_to_env,
+        logger,
+        checkpointer,
+        grpo_state,
+        master_config,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/nemo_rl/distributed/ray_actor_environment_registry.py b/nemo_rl/distributed/ray_actor_environment_registry.py
index 277619bb92..9464569c32 100644
--- a/nemo_rl/distributed/ray_actor_environment_registry.py
+++ b/nemo_rl/distributed/ray_actor_environment_registry.py
@@ -22,6 +22,7 @@
     "nemo_rl.models.policy.megatron_policy_worker.MegatronPolicyWorker": PY_EXECUTABLES.MCORE,
     "nemo_rl.environments.math_environment.MathEnvironment": PY_EXECUTABLES.SYSTEM,
     "nemo_rl.environments.games.sliding_puzzle.SlidingPuzzleEnv": PY_EXECUTABLES.SYSTEM,
+    "nemo_rl.environments.simulated_user.unique_numbers.UniqueNumbersEnv": PY_EXECUTABLES.SYSTEM,
 }
 
 
diff --git a/nemo_rl/environments/simulated_user/__init__.py b/nemo_rl/environments/simulated_user/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/nemo_rl/environments/simulated_user/adk_utils.py b/nemo_rl/environments/simulated_user/adk_utils.py
new file mode 100644
index 0000000000..6bc79114e2
--- /dev/null
+++ b/nemo_rl/environments/simulated_user/adk_utils.py
@@ -0,0 +1,186 @@
+import asyncio
+import logging
+import random
+
+from google.adk import Agent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types
+from google.genai.errors import ServerError
+
+# Initialize logging
+logging.basicConfig(
+    format="[%(asctime)s] [%(levelname)s] %(message)s",
+    level=logging.WARNING,
+)
+logger = logging.getLogger(__name__)
+
+
+# Define the agents
+def create_agent(
+    instruction: str | None = None,
+    name: str = "simulated_user",
+    model: str = "gemini-2.0-flash",
+) -> Agent:
+    return Agent(
+        model=model,
+        name=name,
+        description="Agent",
+        instruction=instruction
+        or "You are a helpful assistant that help people answer questions.",
+        generate_content_config=types.GenerateContentConfig(
+            safety_settings=[
+                types.SafetySetting(
+                    category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
+                    threshold=types.HarmBlockThreshold.OFF,
+                ),
+            ]
+        ),
+    )
+
+
+def get_session_from_runner(runner: Runner, user_id: str):
+    app_session_map = runner.session_service.sessions
+    assert len(app_session_map) == 1, "Expected exactly one app in session_service"
+    user_sessions_map = next(iter(app_session_map.values()))
+    sessions = user_sessions_map[user_id]
+    assert len(sessions) == 1, "Expected exactly one user in app session"
+    return next(iter(sessions.values()))
+
+
+def get_agent_instruction_from_runner(runner: Runner):
+    return runner.agent.instruction
+
+
+def extract_conversation_history(runner: Runner, user_id: str, silence: bool = True):
+    session = get_session_from_runner(runner, user_id)
+    instruction = get_agent_instruction_from_runner(runner)
+    convo = [{"role": "instruction", "content": instruction}]
+    for event in session.events:
+        if event.content.parts and event.content.parts[0].text:
+            convo.append({"role": event.author, "content": event.content.parts[0].text})
+            if not silence:
+                logger.info(f"[{convo[-1]['role']}]: {convo[-1]['content']}")
+    return session.id, convo
+
+
+async def run_prompt_async(
+    runner: Runner,
+    user_id: str,
+    new_message: str,
+    silence: bool = True,
+    max_retries: int = 3,
+    initial_delay: float = 2,
+) -> str:
+    new_message = new_message.strip()
+    content = types.Content(role="user", parts=[types.Part.from_text(text=new_message)])
+    if not silence:
+        logger.info(f"** [User]->|||{new_message}|||")
+
+    session = get_session_from_runner(runner, user_id)
+
+    retries = 0
+    delay = initial_delay
+    while retries < max_retries:
+        try:
+            async for event in runner.run_async(
+                user_id=session.user_id,
+                session_id=session.id,
+                new_message=content,
+            ):
+                if event.content.parts and event.content.parts[0].text:
+                    if not silence:
+                        logger.info(
+                            f"** [{event.author}]->|||{event.content.parts[0].text.strip()}|||"
+                        )
+                    return event.content.parts[0].text.strip()
+                else:
+                    return "<Empty response>"
+        except ServerError as e:
+            retries += 1
+            delay_with_jitter = delay + (random.random() * 2 - 1) * (delay * 0.5)
+            logger.error(
+                f"Gemini API call (with message {new_message}) failed with ServerError {e} (attempt {retries}/{max_retries}). Retrying in {delay_with_jitter} seconds..."
+            )
+            await asyncio.sleep(delay_with_jitter)
+            delay *= 2  # Exponential backoff
+        except Exception as e:
+            logger.error(
+                f"Gemini API call (with message {new_message}) failed with an unexpected error: {e}."
+            )
+            return f"<No response due to unexpected error: {e}>"
+
+    logger.error(
+        f"Gemini API call (with message {new_message}) reached maximum retries ({max_retries}) without success."
+    )
+    return f"<No response due after {max_retries} retries>"
+
+
+async def setup_runner_async(agent: Agent, app_name: str, user_id: str):
+    runner = Runner(
+        agent=agent, app_name=app_name, session_service=InMemorySessionService()
+    )
+    await runner.session_service.create_session(app_name=app_name, user_id=user_id)
+    return runner
+
+
+async def main():
+    sample_id_1 = "sample_1"
+    sample_id_2 = "sample_2"
+
+    # Set up simulated user runner
+    simulated_user_app_name = "su_app"
+    simulated_user_runner = Runner(
+        agent=create_agent(name="simulated_user"),
+        app_name=simulated_user_app_name,
+        session_service=InMemorySessionService(),
+    )
+
+    await simulated_user_runner.session_service.create_session(
+        app_name=simulated_user_app_name, user_id=sample_id_1
+    )
+    await simulated_user_runner.session_service.create_session(
+        app_name=simulated_user_app_name, user_id=sample_id_2
+    )
+
+    # setup grader runner
+    grader_app_name = "grader_app"
+    grader_instruction = "You are a helpful agent that can grade the correctness and coherent of a conversation. Please only give an integer as the score."
+    grader_runner = await setup_runner_async(
+        agent=create_agent(name="grader", instruction=grader_instruction),
+        app_name=grader_app_name,
+        user_id=sample_id_1,
+    )
+
+    # Simulated user interactions
+    await run_prompt_async(
+        simulated_user_runner, sample_id_1, "what is 2*3+5?", silence=False
+    )
+    await run_prompt_async(simulated_user_runner, sample_id_2, "what is 2*3-5?")
+    await run_prompt_async(simulated_user_runner, sample_id_1, "Now add another 10.")
+    await run_prompt_async(simulated_user_runner, sample_id_2, "Now add another 100.")
+
+    # Print conversation
+    logger.info("-" * 100)
+    _, convo1 = extract_conversation_history(
+        simulated_user_runner, sample_id_1, silence=False
+    )
+    logger.info("-" * 100)
+    _, convo2 = extract_conversation_history(
+        simulated_user_runner, sample_id_2, silence=False
+    )
+    logger.info("-" * 100)
+
+    # Grade conversation
+    await run_prompt_async(
+        grader_runner,
+        sample_id_1,
+        f"Grade the above conversation and give a score between 0-10. \n\n{convo1}",
+        silence=False,
+    )
+    logger.info("-" * 100)
+    logger.info("DONE!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/nemo_rl/environments/simulated_user/prompt.py b/nemo_rl/environments/simulated_user/prompt.py
new file mode 100644
index 0000000000..6fd742c929
--- /dev/null
+++ b/nemo_rl/environments/simulated_user/prompt.py
@@ -0,0 +1,30 @@
+starting_user_prompt = (
+    "I will play a game with you. I have a list of integers in mind and can NOT tell you. "
+    "Your goal is to guess the count of UNIQUE numbers in my list. The only 2 things you can do is the following: "
+    "You can either ask me 'what is number k?' to get the number at position k in my list, "
+    "or answer 'there are m unique numbers' whenever you feel you want to make a guess."
+    "Please do not say anything else. You cannot ask me to provide the list of integers."
+)
+
+
+simulated_user_instruction = """
+You are a simulated user in a game where the assistant must figure out how many unique numbers you have.
+You have a list of numbers (which may contain duplicates) that you will not reveal to the assistant.
+The assistant can ask you questions of the form "What is number k?" where k is a 1-based index into your list of numbers.
+You should respond with the number at that index.
+The assistant can also make a guess by saying "There are m unique numbers" where m is their guess for the count of unique numbers.
+If the assistant makes a correct guess, you will reward it. If the guess is incorrect, you will penalize it.
+
+Here is your list of numbers: {numbers}.
+""".strip()
+
+grader_instruction = """
+Your are a strict grader to evaluate whether the assistant has properly guessed the count of unique numbers.
+Here is your list of numbers: {numbers}.
+You will see a conversation between the assistant and a simulated user who has this list of numbers.
+You will need to evaluete in the end whether the assistant has made a correct guess of the count of unique numbers.
+If the assistant made a correct guess, give it a score of 1. If the guess is incorrect, give it a score of 0.
+If assistant made a correct guess but you feel the assistant has asked too many questions, please give a score between 0 and 1.
+If the assistant never made a guess, give it a score of 0.
+Please only output an integer score between 0 and 1, and nothing else.
+""".strip()
diff --git a/nemo_rl/environments/simulated_user/unique_numbers.py b/nemo_rl/environments/simulated_user/unique_numbers.py
new file mode 100644
index 0000000000..df66bef62b
--- /dev/null
+++ b/nemo_rl/environments/simulated_user/unique_numbers.py
@@ -0,0 +1,208 @@
+"""Simulated user environment for counting unique numbers."""
+
+from __future__ import annotations
+
+import asyncio
+import random
+import re
+import os
+from datetime import datetime
+from typing import Optional, TypedDict
+from concurrent.futures import ThreadPoolExecutor
+
+import ray
+import torch
+
+from nemo_rl.data.interfaces import LLMMessageLogType
+from nemo_rl.distributed.batched_data_dict import BatchedDataDict
+from nemo_rl.environments.interfaces import EnvironmentInterface, EnvironmentReturn
+from nemo_rl.environments.simulated_user.adk_utils import run_prompt_async, setup_runner_async, create_agent, extract_conversation_history
+from nemo_rl.environments.simulated_user.prompt import simulated_user_instruction, grader_instruction
+
+# ────────────────────────── ADK imports ────────────────────────────────────────
+from google.genai import types
+from google.adk.agents import Agent
+from google.adk.runners import Runner as ADKRunner
+
+
+PENALTY_FOR_NO_GUESS = -0.2
+PENALTY_FOR_INCORRECT_GUESS = 0.0
+PENALTY_FOR_EVERY_ASK = 0.0
+PENALTY_FOR_INCORRECT_FORMAT = 0.0
+
+ADK_LOG_FOLDER = None # 'logs/adk' or None to disable logging
+if ADK_LOG_FOLDER is not None:
+    os.makedirs(ADK_LOG_FOLDER, exist_ok=True)
+
+GEMINI_CALL_MAX_WORKERS = 64 # if 1 then it will be single-threaded, otherwise it will use ThreadPoolExecutor
+
+class UniqueNumbersConfig(TypedDict, total=False):
+    """Configuration for :class:`UniqueNumbersEnv`."""
+
+    min_length: int
+    max_length: int
+    max_turns: int
+
+
+class UniqueNumbersMetadata(TypedDict):
+    """Metadata for a UniqueNumbersEnv episode."""
+
+    numbers: list[int]
+    unique_count: int
+    turn: int
+    max_turns: int
+    simulated_user_runner: Optional[ADKRunner]
+    grader_runner: Optional[ADKRunner]
+
+class _UniqueNumbersRunner:
+
+    query_re = re.compile(r"what is number (\d+)\??$", re.IGNORECASE)
+    guess_re = re.compile(r"there are (\d+) unique number", re.IGNORECASE)
+
+    def _maybe_dump_adk_messages_to_file(self, runner: ADKRunner, user_id:str, log_name_suffix:str = "", dump_folder: str = ADK_LOG_FOLDER):
+        if dump_folder is None:
+            return
+        session_id, messages = extract_conversation_history(runner, user_id, silence=True)
+        file_name = f"{user_id}_{session_id}{log_name_suffix}.log"
+        with open(os.path.join(dump_folder, file_name), "a") as f:
+            for message in messages:
+                f.write(f"[{message['role']}]:|||{message['content']}|||\n")
+
+    def process_turn(
+        self, message_log: LLMMessageLogType, metadata: UniqueNumbersMetadata
+    ) -> tuple[dict[str, str], float, bool, None, Optional[UniqueNumbersMetadata]]:
+
+        turn = metadata["turn"]
+        max_turns = metadata["max_turns"]
+
+
+        if "simulated_user_runner" not in metadata or metadata["simulated_user_runner"] is None:
+            instruction = simulated_user_instruction.replace("{numbers}", str(metadata["numbers"]))
+            simulated_user_agent = create_agent(name="simulated_user", model="gemini-2.0-flash", instruction=instruction)
+            metadata["simulated_user_runner"] = asyncio.run(setup_runner_async(simulated_user_agent, "simulated_user_app", "simulated_user"))
+        
+        if "grader_runner" not in metadata or metadata["grader_runner"] is None:
+            instruction = grader_instruction.replace("{numbers}", str(metadata["numbers"]))
+            grader_agent = create_agent(name="grader", model="gemini-2.0-flash", instruction=instruction)
+            metadata["grader_runner"] = asyncio.run(setup_runner_async(grader_agent, "grader_app", "grader"))
+
+        if turn >= max_turns:
+            self._maybe_dump_adk_messages_to_file(metadata["simulated_user_runner"], "simulated_user", "_maxturns")
+            return {"role": "user", "content": "<done>"}, PENALTY_FOR_NO_GUESS, True, None, None
+
+        last_msg = ""
+        if message_log and message_log[-1]["role"] == "assistant":
+            last_msg = message_log[-1]["content"].strip()
+
+        if not last_msg:
+            # no last message from assistant, assuming done
+            return {"role": "user", "content": "<done>"}, PENALTY_FOR_NO_GUESS, True, None, None
+
+        # simulate user utterance via ADK
+        query_match = self.query_re.search(last_msg)
+        if query_match:
+            simulated_content = asyncio.run(run_prompt_async(metadata["simulated_user_runner"], "simulated_user", last_msg, silence=True))
+            next_meta = {
+                "numbers": metadata["numbers"],
+                "unique_count": metadata["unique_count"],
+                "turn": turn + 1,
+                "max_turns": max_turns,
+                "simulated_user_runner": metadata.get("simulated_user_runner", None),
+                "grader_runner": metadata.get("grader_runner", None)
+            }
+            return {"role": "user", "content": simulated_content}, PENALTY_FOR_EVERY_ASK, False, None, next_meta
+
+        # calculate reward if the assistant made a guess
+        guess_match = self.guess_re.search(last_msg)
+        if guess_match:
+            m = int(guess_match.group(1))
+            reward = 1.0 if m == metadata["unique_count"] else PENALTY_FOR_INCORRECT_GUESS
+
+            # grade the conversation via ADK grader
+            if metadata["grader_runner"] is not None:
+                convo_str = "\n".join([f"{msg['role']}: {msg['content']}" for msg in message_log])
+                grading_prompt = f"Here is the converstation \n{convo_str}\nAnd please give the score between 0 and 1."
+                grading_response = asyncio.run(run_prompt_async(metadata["grader_runner"], "grader", grading_prompt, silence=True))
+                try:
+                    grade = int(re.search(r"(\d+)", grading_response).group(1))
+                    reward = (reward + grade) / 2.0
+                except Exception as e:
+                    print(f"Failed to parse grade from grader response '{grading_response}': {e}")
+            
+            self._maybe_dump_adk_messages_to_file(metadata["simulated_user_runner"], "simulated_user", "_stop")
+            self._maybe_dump_adk_messages_to_file(metadata["grader_runner"], "grader")
+
+            return {"role": "user", "content": "<done>"}, reward, True, None, None
+
+        # default response
+        next_meta = {
+            "numbers": metadata["numbers"],
+            "unique_count": metadata["unique_count"],
+            "turn": turn + 1,
+            "max_turns": max_turns,
+            "simulated_user_runner": metadata.get("simulated_user_runner", None),
+            "grader_runner": metadata.get("grader_runner", None)
+        }
+        help_msg = "Please ask 'what is number k?' or say 'there are m unique numbers'."
+        return {"role": "user", "content": help_msg}, PENALTY_FOR_INCORRECT_FORMAT, False, None, next_meta
+
+
+@ray.remote
+class UniqueNumbersEnv(EnvironmentInterface):
+    """Environment where the LLM must deduce the count of unique numbers."""
+
+    def __init__(self, cfg: Optional[UniqueNumbersConfig] = None):
+        cfg = cfg or {}
+        self.min_length = cfg.get("min_length", 3)
+        self.max_length = cfg.get("max_length", 7)
+        self.default_max_turns = cfg.get("max_turns", 10)
+
+        self.runner = _UniqueNumbersRunner()
+
+    def step(
+        self,
+        message_log_batch: list[LLMMessageLogType],
+        metadata_batch: list[Optional[UniqueNumbersMetadata]],
+    ) -> EnvironmentReturn:
+
+        args = []
+        for log, meta in zip(message_log_batch, metadata_batch):
+            assert meta is not None, "Metadata must not be None for UniqueNumbersEnv."
+            assert meta["numbers"] is not None, "Numbers must not be None in metadata."
+            assert meta["unique_count"] > 0, "Unique count must be greater than 0 in metadata."
+            args.append((log, meta))
+
+        # Process either serially or in parallel
+        if GEMINI_CALL_MAX_WORKERS is None or GEMINI_CALL_MAX_WORKERS <= 1:
+            results = [self.runner.process_turn(log, meta) for log, meta in args]
+        else:
+            with ThreadPoolExecutor(max_workers=GEMINI_CALL_MAX_WORKERS) as executor:
+                results = list(executor.map(lambda p: self.runner.process_turn(*p), args))
+
+        observations, rewards, terminateds, stop_strings, next_metadata = [], [], [], [], []
+        for obs, rew, term, stops, meta in results:
+            observations.append(obs)
+            rewards.append(rew)
+            terminateds.append(term)
+            stop_strings.append(stops)
+            next_metadata.append(meta)
+
+        return EnvironmentReturn(
+            observations=observations,
+            metadata=next_metadata,
+            next_stop_strings=stop_strings,
+            rewards=torch.tensor(rewards, dtype=torch.float32),
+            terminateds=torch.tensor(terminateds, dtype=torch.bool),
+        )
+
+    def shutdown(self) -> None:  # pragma: no cover
+        pass
+
+    def global_post_process_and_metrics(
+        self, batch: BatchedDataDict
+    ) -> tuple[BatchedDataDict, dict]:
+        final_rewards = batch.get(
+            "total_reward", torch.tensor([0.0] * len(batch["idx"]))
+        )
+        avg_reward = final_rewards.mean().item() if len(final_rewards) > 0 else 0.0
+        return batch, {"unique_numbers_avg_reward": avg_reward}
diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py
index 1e0a027bcb..5aec9ff282 100644
--- a/nemo_rl/experience/rollouts.py
+++ b/nemo_rl/experience/rollouts.py
@@ -352,6 +352,11 @@ def run_multi_turn_rollout(
         if len(active_indices) == 0:
             break
 
+        if max_rollout_turns > 1:
+            print(
+                f"▶ ▶ ▶ Running rollout turn {turn + 1} / {max_rollout_turns} with {len(active_indices)} active samples..."
+            )
+
         active_samples_per_turn.append(len(active_indices))
 
         # Convert LLMMessageLogType to FlatMessagesType for generation
@@ -376,7 +381,6 @@ def run_multi_turn_rollout(
                 "stop_strings": active_stop_strings,
             }
         )
-
         # generate_responses updates active_batch["message_log"] in-place
         active_batch, generated_ids, gen_metrics = generate_responses(
             policy_generation,
@@ -405,12 +409,20 @@ def run_multi_turn_rollout(
         truncation_mask = torch.zeros_like(env_output.terminateds, dtype=torch.bool)
         for i, global_idx in enumerate(active_indices.tolist()):
             env_obs_content = env_output.observations[i]["content"]
-            # Tokenize the raw content from the environment
-            # TODO @sahilj: handle if we want these subsequent messages to have a chat template
-            tokenized_obs = tokenizer(
-                env_obs_content, return_tensors="pt", add_special_tokens=False
-            ).input_ids[0]
-
+            # Tokenize the raw content from the environment into chat format if needed
+            env_role = env_output.observations[i]["role"].lower()
+            if env_role in {"user", "assistant", "system"}:
+                formatted_obs = tokenizer.apply_chat_template(
+                    [{"role": env_role, "content": env_obs_content.strip()}],
+                    tokenize=False,
+                ).removeprefix("<|begin_of_text|>")
+                tokenized_obs = tokenizer(
+                    formatted_obs, return_tensors="pt", add_special_tokens=False
+                ).input_ids[0]
+            else:
+                tokenized_obs = tokenizer(
+                    env_obs_content, return_tensors="pt", add_special_tokens=False
+                ).input_ids[0]
             # check if new message overflows max_seq_len
             if (
                 len(tokenized_obs) + len(generated_ids[i]) + active_input_lengths[i]
@@ -660,9 +672,23 @@ async def run_sample_multi_turn_rollout(
         terminated = env_output.terminateds[0].item()
         env_obs_content = env_output.observations[0]["content"]
         # Tokenize environment response
-        tokenized_obs = tokenizer(
-            env_obs_content, return_tensors="pt", add_special_tokens=False
-        ).input_ids[0]
+        env_role = env_output.observations[0]["role"].lower()
+        if env_role in {"user", "assistant", "system"}:
+            formatted_obs = (
+                tokenizer.apply_chat_template(
+                    [{"role": env_role, "content": env_obs_content.strip()}],
+                    tokenize=False,
+                )
+                .removeprefix("<|begin_of_text|>")
+                .strip()
+            )
+            tokenized_obs = tokenizer(
+                formatted_obs, return_tensors="pt", add_special_tokens=False
+            ).input_ids[0]
+        else:
+            tokenized_obs = tokenizer(
+                env_obs_content, return_tensors="pt", add_special_tokens=False
+            ).input_ids[0]
 
         # Check for sequence length overflow
         if input_lengths + gen_token_count + len(tokenized_obs) >= max_seq_len:
diff --git a/pyproject.toml b/pyproject.toml
index cddda79abe..75e41feba7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,6 +43,7 @@ dependencies = [
     "nvtx",
     "matplotlib",
     "plotly",
+    "google-adk",
     "mlflow",
 ]
 
diff --git a/uv.lock b/uv.lock
index 1e6d6815b2..2e8b19c839 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,15 +2,10 @@ version = 1
 revision = 2
 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 platform_machine != 'arm64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'arm64' and sys_platform == 'linux'",
-    "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 == 'darwin'",
-    "python_full_version < '3.13' and sys_platform == 'darwin'",
-    "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 sys_platform == 'linux'",
+    "python_full_version < '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux'",
+    "python_full_version < '3.13' and sys_platform != 'linux'",
 ]
 
 [manifest]
@@ -26,16 +21,16 @@ requires-dist = ["torch", "einops", "setuptools", "psutil", "ninja"]
 
 [[package]]
 name = "absl-py"
-version = "2.3.0"
+version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/03/15/18693af986560a5c3cc0b84a8046b536ffb2cdb536e03cce897f2759e284/absl_py-2.3.0.tar.gz", hash = "sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f", size = 116400, upload-time = "2025-05-27T09:15:50.143Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/04/9d75e1d3bb4ab8ec67ff10919476ccdee06c098bcfcf3a352da5f985171d/absl_py-2.3.0-py3-none-any.whl", hash = "sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3", size = 135657, upload-time = "2025-05-27T09:15:48.742Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
 ]
 
 [[package]]
 name = "accelerate"
-version = "1.8.1"
+version = "1.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -46,9 +41,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bd/c2/b9e33ad13232606dded4c546e654fb06a15f1dbcbd95d81c9f9dd3ccc771/accelerate-1.8.1.tar.gz", hash = "sha256:f60df931671bc4e75077b852990469d4991ce8bd3a58e72375c3c95132034db9", size = 380872, upload-time = "2025-06-20T15:36:14.618Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c4/25/969456a95a90ed38f73f68d0f0915bdf1d76145d05054c59ad587b171150/accelerate-1.9.0.tar.gz", hash = "sha256:0e8c61f81af7bf37195b6175a545ed292617dd90563c88f49020aea5b6a0b47f", size = 383234, upload-time = "2025-07-16T16:24:54.526Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/91/d9/e044c9d42d8ad9afa96533b46ecc9b7aea893d362b3c52bd78fb9fe4d7b3/accelerate-1.8.1-py3-none-any.whl", hash = "sha256:c47b8994498875a2b1286e945bd4d20e476956056c7941d512334f4eb44ff991", size = 365338, upload-time = "2025-06-20T15:36:12.71Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/1c/a17fb513aeb684fb83bef5f395910f53103ab30308bbdd77fd66d6698c46/accelerate-1.9.0-py3-none-any.whl", hash = "sha256:c24739a97ade1d54af4549a65f8b6b046adc87e2b3e4d6c66516e32c53d5a8f1", size = 367073, upload-time = "2025-07-16T16:24:52.957Z" },
 ]
 
 [[package]]
@@ -74,7 +69,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.13"
+version = "3.12.14"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -85,42 +80,42 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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