From 0fa333cc07d829a79aa5c3a2b2b065350e0d4b1f Mon Sep 17 00:00:00 2001
From: slikhite-1 <slikhite@nvidia.com>
Date: Thu, 21 Aug 2025 10:10:01 -0700
Subject: [PATCH 1/3] Docs added for sliding puzzle example

Signed-off-by: slikhite-1 <slikhite@nvidia.com>
---
 docs/assets/train-reward-sliding-puzzle.png | Bin 0 -> 30308 bytes
 docs/assets/valid_acc-sliding-puzzle.png    | Bin 0 -> 14401 bytes
 docs/guides/grpo-sliding-puzzle.md          | 294 ++++++++++++++++++++
 examples/configs/grpo_sliding_puzzle.yaml   |   8 +-
 4 files changed, 298 insertions(+), 4 deletions(-)
 create mode 100644 docs/assets/train-reward-sliding-puzzle.png
 create mode 100644 docs/assets/valid_acc-sliding-puzzle.png
 create mode 100644 docs/guides/grpo-sliding-puzzle.md

diff --git a/docs/assets/train-reward-sliding-puzzle.png b/docs/assets/train-reward-sliding-puzzle.png
new file mode 100644
index 0000000000000000000000000000000000000000..82d319f4f23b4061a98dfb223e89787fb2ceb388
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diff --git a/docs/guides/grpo-sliding-puzzle.md b/docs/guides/grpo-sliding-puzzle.md
new file mode 100644
index 0000000000..8b69931fe8
--- /dev/null
+++ b/docs/guides/grpo-sliding-puzzle.md
@@ -0,0 +1,294 @@
+# Solve a Sliding Puzzle Using GRPO
+
+This guide explains how to use Nemo RL to train a model to solve the classic **nxn sliding puzzle** game through multi-turn reinforcement learning. This environment implements a classic **n×n sliding puzzle** where numbered tiles must be arranged in sequential order by sliding them into an empty space.
+
+The sliding puzzle task serves as a simple, yet effective example, to illustrate how multi-turn RL and tool-calling are implemented within Nemo RL. This example provides a minimal setup for understanding the core components of Group Relative Policy Optimization (GRPO) and sequential decision-making.
+
+
+## Quick Start Guide
+
+#### 1. Install and Set Up NeMo RL with Megatron Backend (Optional)
+
+To get started, clone and set up the NeMo RL repository by initializing submodules, installing CUDA dependencies, and configuring the environment with uv. Refer to [Prerequisites](https://github.com/NVIDIA-NeMo/RL/tree/main?tab=readme-ov-file#prerequisites) for detailed instructions on installation.
+
+#### 2. Train a Model
+
+Train a model to solve the sliding puzzle using GRPO with the default 2×2 configuration.
+
+```bash
+uv run python examples/run_grpo_sliding_puzzle.py 
+```
+
+#### 3. Customize Puzzle Configuration
+
+By default, this training script uses the configuration in [grpo_sliding_puzzle.yaml](../../examples/configs/grpo_sliding_puzzle.yaml). You can customize parameters with command-line overrides to experiment with different puzzle sizes or levels of difficulty.
+```bash
+# Train on a 3×3 puzzle with 10 random moves to scramble the board
+uv run python examples/run_grpo_sliding_puzzle.py \
+    env.sliding_puzzle_game.cfg.game_config.size=3 \
+    env.sliding_puzzle_game.cfg.game_config.shuffle_moves=10
+```
+
+#### 4. Monitor Progress
+
+You can enable logging via Weights & Biases and TensorBoard to monitor training metrics such as rewards, success rate, and loss curves.
+
+```bash
+# Enable logging (optional)
+uv run examples/run_grpo_sliding_puzzle.py \
+    --config examples/configs/grpo_sliding_puzzle.yaml \
+    logger.wandb_enabled=true \
+    logger.tensorboard_enabled=true
+```
+
+## Game Mechanics
+
+### Puzzle Structure
+
+The sliding puzzle consists of:
+- **Grid**: An `n×n` grid with numbered tiles and one empty space
+- **Tiles**: Numbered from `1` to `n²-1`, placed in random order
+- **Empty Space**: Represented by `0`, typically starting at the bottom-right corner
+- **Goal State**: Sequential arrangement `1, 2, 3, ..., n²-1` with `0` at bottom-right
+
+### Example Data Sample
+```
+===== SLIDING PUZZLE =====
+Arrange the 3x3 grid by sliding tiles into the empty space.
+- The goal is to arrange numbers from 1 to 8 in order
+- Use 'up', 'down', 'left', 'right' to slide in that direction
+- Use 'view' to see the current state of the board
+
+Current Board State:
+
+  +---------+
+1 | 1     3 |
+2 | 4  2  5 |
+3 | 7  8  6 |
+  +---------+
+     1  2  3 
+
+Reach the goal state where numbers are ordered 1 through 8 with the empty space (0) at the bottom right.
+Valid actions: 'up', 'down', 'left', 'right', or 'slide row col' (e.g., 'slide 1 2').
+After thinking, output your chosen action on a new line starting with '<action></action>' like this:
+<action>your_action</action>
+If you just want to see the board, output <action>view</action>
+Think carefully step-by-step before acting.
+
+```
+
+### Movement Rules
+
+1. **Valid Moves**: Only tiles adjacent to the empty space `0` can be moved.
+2. **Movement Direction**: Tiles slide into the empty space, not the other way around.
+3. **Grid Boundaries**: Moves that would go beyond the grid are invalid.
+4. **Single Tile Movement**: Each action affects only one tile at a time.
+
+All actions must be wrapped in XML-style tags and follow one of the formats below:
+```xml
+<action>up</action>          <!-- Slide a tile up into the empty space -->
+<action>slide 2 1</action>   <!-- Slide tile at row 2, column 1 -->
+<action>view</action>        <!-- View the current board state -->
+```
+
+## Data Generation
+
+### Configuration Parameters
+
+Sliding puzzle instances are generated using the following parameters, which can be customized via the configuration file:
+
+```yaml
+env:
+  sliding_puzzle_game:
+    cfg:
+      game_config:
+        size: 5              # Size of the puzzle grid (e.g., 3x3, 4x4, 5x5)
+        shuffle_moves: 4     # Number of random moves to scramble the puzzle
+      max_moves: 40          # Maximum number of moves allowed per episode
+```
+#### Description
+
+- **`size`**: Determines the dimensions of the puzzle board (`n×n`).
+- **`shuffle_moves`**: Controls the initial difficulty by randomly moving tiles to scramble the puzzle.
+- **`max_moves`**: Sets an upper limit on the number of actions the agent can take in one episode.
+
+Grids are generated with sizes ranging from 2 to game_config.size. Each grid starts with a solved state and is shuffled by moving random tiles to the empty space n times, where n is a random number between 1 and `shuffle_moves`. The grid is shuffled using only valid moves. 
+The `generate_puzzle_datum()` function in [run_grpo_sliding_puzzle.py](../../examples/run_grpo_sliding_puzzle.py) is responsible for generating the dataset. [sliding_puzzle.py](../../nemo_rl/environments/games/sliding_puzzle.py) contains the `SlidingPuzzleGameLogic` class, responsible for puzzle generation and initialization logic. The number of shuffle moves and size of the grid will control puzzle difficulty.
+
+#### Generation Algorithm
+The puzzle configuration is randomly generated by sampling the grid size and number of shuffling moves within the defined maximums:
+
+```python
+def generate_random_config(max_config: dict[str, Any]) -> dict[str, Any]:
+    """Generate a random config for the sliding puzzle game."""
+    shuffle_moves = random.randint(1, max_config.get("shuffle_moves"))
+    if shuffle_moves % 2 == 0:
+        shuffle_moves += 1  # Ensure odd number for proper scrambling
+    return {
+        "size": random.randint(2, max_config.get("size", 3)),
+        "shuffle_moves": shuffle_moves,
+    }
+
+      game_config = generate_random_config(game_config)
+      initial_game_state = SlidingPuzzleGameLogic.generate(game_config)
+      initial_render = SlidingPuzzleGameLogic.render(initial_game_state)
+      welcome_message = SlidingPuzzleGameLogic.init(initial_game_state)
+  ```
+
+### Dataset Size Calculation
+
+Dataset size is defined by parameters in grpo_sliding_puzzle.yaml:
+```
+Training Size = num_prompts_per_step × num_generations_per_prompt × max_num_steps
+Validation Size = max_val_samples
+```
+
+### Data Structure
+
+Each training sample is returned as a `DatumSpec` dictionary with the following structure:
+
+```python
+datum: DatumSpec = {
+    "message_log": message_log,              # Conversation history
+    "length": len(tokenized_prompt),         # Token count
+    "extra_env_info": metadata,              # Game state metadata
+    "loss_multiplier": 1.0,                  # Training weight
+    "idx": idx,                              # Sample index
+    "task_name": task_name,                  # Task identifier
+    "stop_strings": ["</action>"],           # Termination tokens
+}
+```
+
+## Environment Interface
+
+<!-- ### Architecture Flow
+
+```
+GRPO Training Pipeline:
+run_grpo_sliding_puzzle.grpo_train → nemo_rl.experience.rollouts.run_multi_turn_rollouts → generate_response + calculate_reward → environments.games.sliding_puzzle.SlidingPuzzleEnv.step
+``` -->
+
+### Core Classes
+
+The [sliding_puzzle.py](../../nemo_rl/environments/games/sliding_puzzle.py) defines the environment and the logic for interacting with the environment. The core classes used are outlined below:
+
+#### SlidingPuzzleEnv
+The SlidingPuzzleEnv class serves as the main environment, implementing a Ray remote actor for distributed processing and using functions from both the SlidingPuzzleGameLogic and SlidingPuzzleRunner classes to interact with the environment.
+
+```echanics with stat
+@ray.remote
+class SlidingPuzzleEnv(EnvironmentInterface):
+    def __init__(self, cfg: Optional[SlidingPuzzleConfig] = None):
+        """Initialize environment with configuration."""
+        
+    def step(
+        self,
+        message_log_batch: list[LLMMessageLogType],
+        metadata_batch: list[SlidingPuzzleMetadata],
+    ) -> EnvironmentReturn:
+        """Process batch of interactions."""
+```
+
+#### SlidingPuzzleGameLogic
+The SlidingPuzzleGameLogic class defines the core game mechanics through static methods for puzzle operations and includes functionality for reward calculation.
+
+```python
+class SlidingPuzzleGameLogic:
+    @staticmethod
+    def generate(config: dict[str, Any]) -> dict[str, Any]:
+        """Generate new puzzle with specified configuration."""
+        
+    @staticmethod
+    def init(game_state: dict[str, Any]) -> str:
+        """Create welcome message with game rules."""
+        
+    @staticmethod
+    def step(action: str, game_state: dict[str, Any]) -> tuple[str, float, bool, dict[str, Any]]:
+        """Execute action and return (response, reward, terminated, new_state)."""
+        
+    @staticmethod
+    def render(game_state: dict[str, Any]) -> str:
+        """Render current puzzle state as visual grid."""
+```
+
+#### SlidingPuzzleRunner
+
+The SlidingPuzzleRunner class handles turn processing and action management.
+
+```python
+class SlidingPuzzleRunner:
+    def __init__(self):
+        """Initialize runner with no persistent state."""
+        
+    def _parse_action(self, text: str) -> Optional[str]:
+        """Extract action from model response using XML tag parsing."""
+        
+    def process_turn(
+        self,
+        message_log: LLMMessageLogType,
+        metadata: SlidingPuzzleMetadata,
+    ) -> tuple[dict[str, str], float, bool, Optional[list[str]], Optional[SlidingPuzzleMetadata]]:
+        """Process single turn and return (response_dict, reward, terminated, stop_strings, updated_metadata)."""
+```
+
+### Processing Pipeline
+
+The step function creates a processing pipeline where each class handles specific responsibilities:
+
+1. **Parse action** (`SlidingPuzzleRunner`): Extracts the action from the model response using XML tag parsing via the `process_turn` method.
+2. **Validate Move** (`SlidingPuzzleGameLogic`): Checks if the action is valid for the current game state and then executes the move.
+3. **Execute Action** (`SlidingPuzzleGameLogic`): Applies the move to the game state using the `SlidingPuzzleGameLogic.step` method.
+4. **Calculate Reward** (`SlidingPuzzleGameLogic`): Assigns a reward based on progress toward solving the puzzle (step function).
+5. **Return Results** (`SlidingPuzzleEnv`):  Returns the updated interaction state as an `EnvironmentReturn` object.
+
+## Reward System
+
+### Reward Structure
+
+The environment uses a sparse reward scheme designed to encourage complete solution strategies, rather than incremental progress or reward hacking.
+
+| Condition | Reward | Termination |
+|-----------|--------|-------------|
+| Valid move (non-solving) | 0.0 | False |
+| Invalid move | 0.0 | False |
+| Puzzle solved | 1.0 | True |
+| Max moves reached | 0.0 | True |
+| Invalid action format | 0.0 | False |
+
+>Goal: The agent receives a reward only upon successfully solving the puzzle, promoting long-horizon planning.
+
+### Reward Calculation Logic
+
+```python
+def step(action: str, game_state: dict[str, Any]) -> tuple[str, float, bool, dict[str, Any]]:
+    """Process action and calculate reward."""
+    reward = 0.0
+    is_terminated = False
+    
+    if move_made:
+        # Check if puzzle is solved
+        if new_state["grid"] == new_state["solution"]:
+            reward = 1.0
+            is_terminated = True
+        else:
+            reward = 0.0  # No reward for non-solving moves
+    
+    return response, reward, is_terminated, new_state
+```
+## Results
+
+We fine-tuned [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on synthetic data for 120 steps using the following configuration settings:
+
+```
+game_config:
+    size: 5 # Size of the puzzle (e.g., 2 for 2x2, 3 for 3x3)
+    shuffle_moves: 10 # Number of random moves to shuffle the solved state
+max_moves: 30
+```
+
+The figure below displays training rewards vs. steps, along with validation accuracy.
+
+![Training Curve](../assets/train-reward-sliding-puzzle.png)
+
+
+![Validation Accuracy](../assets/valid_acc-sliding-puzzle.png)
\ No newline at end of file
diff --git a/examples/configs/grpo_sliding_puzzle.yaml b/examples/configs/grpo_sliding_puzzle.yaml
index 2d72dd1055..fbbcdeecb3 100644
--- a/examples/configs/grpo_sliding_puzzle.yaml
+++ b/examples/configs/grpo_sliding_puzzle.yaml
@@ -19,7 +19,7 @@ checkpointing:
 
 policy:
   model_name: "Qwen/Qwen2.5-1.5B-Instruct"
-  max_total_sequence_length: 3072
+  max_total_sequence_length: 1024
 
   dtensor_cfg:
     enabled: true
@@ -54,8 +54,8 @@ env:
     cfg:
       game_config:
         size: 5 # Size of the puzzle (e.g., 2 for 2x2, 3 for 3x3)
-        shuffle_moves: 15 # Number of random moves to shuffle the solved state
-      max_moves: 50 # Maximum moves allowed per episode
+        shuffle_moves: 10 # Number of random moves to shuffle the solved state
+      max_moves: 30 # Maximum moves allowed per episode
 
 logger:
   log_dir: "logs"  # Base directory for all logs
@@ -73,4 +73,4 @@ logger:
     run_name: "grpo-dev-sliding_puzzle"
   gpu_monitoring:
     collection_interval: 10  # How often to collect GPU usage metrics (in seconds)
-    flush_interval: 10  # How often to flush GPU usage metrics to the loggers (in seconds)
+    flush_interval: 10  # How often to flush GPU usage metrics to the loggers (in seconds)
\ No newline at end of file

From 9fb0875d12d1cf6f4b9141f7f1e11c87be99c9c1 Mon Sep 17 00:00:00 2001
From: slikhite-1 <slikhite@nvidia.com>
Date: Wed, 3 Sep 2025 21:40:26 -0700
Subject: [PATCH 2/3] Update grpo-sliding-puzzle.md

Signed-off-by: slikhite-1 <slikhite@nvidia.com>
---
 docs/guides/grpo-sliding-puzzle.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/guides/grpo-sliding-puzzle.md b/docs/guides/grpo-sliding-puzzle.md
index 8b69931fe8..d608c0048a 100644
--- a/docs/guides/grpo-sliding-puzzle.md
+++ b/docs/guides/grpo-sliding-puzzle.md
@@ -175,7 +175,7 @@ The [sliding_puzzle.py](../../nemo_rl/environments/games/sliding_puzzle.py) defi
 #### SlidingPuzzleEnv
 The SlidingPuzzleEnv class serves as the main environment, implementing a Ray remote actor for distributed processing and using functions from both the SlidingPuzzleGameLogic and SlidingPuzzleRunner classes to interact with the environment.
 
-```echanics with stat
+```python
 @ray.remote
 class SlidingPuzzleEnv(EnvironmentInterface):
     def __init__(self, cfg: Optional[SlidingPuzzleConfig] = None):
@@ -291,4 +291,4 @@ The figure below displays training rewards vs. steps, along with validation accu
 ![Training Curve](../assets/train-reward-sliding-puzzle.png)
 
 
-![Validation Accuracy](../assets/valid_acc-sliding-puzzle.png)
\ No newline at end of file
+![Validation Accuracy](../assets/valid_acc-sliding-puzzle.png)

From d212d05fe9dd72464797d40117fe08fa5436c809 Mon Sep 17 00:00:00 2001
From: slikhite-1 <slikhite@nvidia.com>
Date: Fri, 5 Sep 2025 21:44:17 -0700
Subject: [PATCH 3/3] CI issues fixed

Signed-off-by: slikhite-1 <slikhite@nvidia.com>
---
 docs/guides/grpo-sliding-puzzle.md | 8 ++++----
 docs/index.md                      | 1 +
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/guides/grpo-sliding-puzzle.md b/docs/guides/grpo-sliding-puzzle.md
index d608c0048a..35833aad34 100644
--- a/docs/guides/grpo-sliding-puzzle.md
+++ b/docs/guides/grpo-sliding-puzzle.md
@@ -7,11 +7,11 @@ The sliding puzzle task serves as a simple, yet effective example, to illustrate
 
 ## Quick Start Guide
 
-#### 1. Install and Set Up NeMo RL with Megatron Backend (Optional)
+### 1. Install and Set Up NeMo RL with Megatron Backend (Optional)
 
 To get started, clone and set up the NeMo RL repository by initializing submodules, installing CUDA dependencies, and configuring the environment with uv. Refer to [Prerequisites](https://github.com/NVIDIA-NeMo/RL/tree/main?tab=readme-ov-file#prerequisites) for detailed instructions on installation.
 
-#### 2. Train a Model
+### 2. Train a Model
 
 Train a model to solve the sliding puzzle using GRPO with the default 2×2 configuration.
 
@@ -19,7 +19,7 @@ Train a model to solve the sliding puzzle using GRPO with the default 2×2 confi
 uv run python examples/run_grpo_sliding_puzzle.py 
 ```
 
-#### 3. Customize Puzzle Configuration
+### 3. Customize Puzzle Configuration
 
 By default, this training script uses the configuration in [grpo_sliding_puzzle.yaml](../../examples/configs/grpo_sliding_puzzle.yaml). You can customize parameters with command-line overrides to experiment with different puzzle sizes or levels of difficulty.
 ```bash
@@ -29,7 +29,7 @@ uv run python examples/run_grpo_sliding_puzzle.py \
     env.sliding_puzzle_game.cfg.game_config.shuffle_moves=10
 ```
 
-#### 4. Monitor Progress
+### 4. Monitor Progress
 
 You can enable logging via Weights & Biases and TensorBoard to monitor training metrics such as rewards, success rate, and loss curves.
 
diff --git a/docs/index.md b/docs/index.md
index 75c85cbfde..ab6d414dc3 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -28,6 +28,7 @@ guides/sft.md
 guides/dpo.md
 guides/grpo.md
 guides/grpo-deepscaler.md
+guides/grpo-sliding-puzzle.md
 guides/rm.md
 guides/eval.md
 guides/deepseek.md