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add randomization for complexity as well as curriculum support
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teknium1
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2 changed files with 1155 additions and 31 deletions
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@ -7,6 +7,8 @@ A reinforcement learning environment for training language models on diverse rea
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The ReasoningGym environment provides access to 100+ reasoning tasks spanning mathematics, logic, programming, and more. It supports:
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- **Diverse Task Types**: Arithmetic, algebra, logic puzzles, programming challenges, and more
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- **Advanced Complexity Control**: Three modes for managing task difficulty (None, Random, Curriculum)
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- **Adaptive Curriculum Learning**: Automatic difficulty adjustment based on model performance
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- **Strict Answer Format Enforcement**: Models must use `<answer>` tags or receive 0 score
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- **Dual-Format Scoring**: Tries both raw answers and tagged answers, using the higher score
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- **Data Collection**: Optional rollout dumping for successful and failed attempts
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@ -15,9 +17,51 @@ The ReasoningGym environment provides access to 100+ reasoning tasks spanning ma
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## Features
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### Task Diversity
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- 100+ tasks from reasoning-gym including GSM Symbolic, ARC, Sudoku, and more
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- **102 tasks** from reasoning-gym with full complexity control coverage
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- Automatic task discovery from the reasoning-gym registry
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- Fallback to comprehensive task list if discovery fails
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- Categories include: Arithmetic, Games, Logic, Algorithmic, Cognition, Algebra, Geometry, Code, Graph, ARC, GSM Symbolic, and more
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### Complexity Control System
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#### Three Complexity Modes
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1. **None (Default)**: Uses reasoning-gym's default parameters for all tasks
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2. **Random**: Randomizes complexity for each problem (0.0-1.0 scale)
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3. **Curriculum**: Adaptive difficulty that adjusts based on model performance
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#### Curriculum Learning Features
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- **Per-task tracking**: Each task has independent complexity management
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- **Target accuracy**: Maintains configurable target accuracy (default 70%)
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- **Immediate adjustment**: Complexity updates after each group is scored
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- **Stability detection**: Considers performance variance for robust adjustments
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- **Fast-track adjustments**: Special handling for very high/low accuracy
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- **Comprehensive monitoring**: Detailed curriculum statistics for wandb logging
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#### Task Coverage
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All 102 reasoning-gym tasks have complexity mappings with realistic parameter ranges:
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**Arithmetic Tasks** (15+ tasks):
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- `basic_arithmetic`, `leg_counting`, `decimal_arithmetic`, `complex_arithmetic`
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- `fraction_simplification`, `bitwise_arithmetic`, `chain_sum`, `count_bits`
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- `gcd`, `lcm`, `prime_factorization`, `power_function`, `products`
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- `time_intervals`, `calendar_arithmetic`, `dice`, `number_format`
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**Games** (15+ tasks):
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- `n_queens`, `sudoku`, `mini_sudoku`, `futoshiki`, `tower_of_hanoi`
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- `maze`, `sokoban`, `rush_hour`, `puzzle24`, `countdown`, `tsumego`
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- `knight_swap`, `emoji_mystery`, `mahjong_puzzle`, `boxnet`
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**Logic** (8+ tasks):
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- `self_reference`, `propositional_logic`, `knights_knaves`, `syllogism`
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- `circuit_logic`, `zebra_puzzles`, `aiw`
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**Algorithmic** (30+ tasks):
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- `graph_color`, `shortest_path`, `largest_island`, `course_schedule`
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- `string_manipulation`, `palindrome_generation`, `word_ladder`
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- `binary_matrix`, `spiral_matrix`, `number_sorting`, and many more
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**And all other categories**: Cognition, Algebra, Geometry, Code, Graph, ARC, GSM Symbolic, Induction
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### Scoring System
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- **Binary Tasks**: 0.0 or 1.0 (most tasks)
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@ -37,24 +81,59 @@ The ReasoningGym environment provides access to 100+ reasoning tasks spanning ma
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```python
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class ReasoningGymEnvConfig(BaseEnvConfig):
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# Data collection
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dump_rollouts: bool = False # Save successful rollouts
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dump_failed_rollouts: bool = False # Save failed rollouts for debugging
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rollout_save_score_threshold: float = 0.7 # Minimum score to save group
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# Complexity control
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complexity_mode: Optional[Literal["curriculum", "random"]] = None
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curriculum_target_accuracy: float = 0.7 # Target accuracy for curriculum mode
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# Evaluation
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num_eval_samples_per_task: int = 5 # Samples per task for evaluation
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eval_seed: int = 123 # Fixed seed for reproducible evaluation
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# Logging and debugging
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debug_logging: bool = False # Enable verbose logging
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suppress_base_env_logs: bool = True # Hide base environment logs
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seed: int = 42 # Random seed for reproducibility
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```
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### Example Configuration
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### Example Configurations
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#### Basic Training (Default Complexity)
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```python
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env_config = ReasoningGymEnvConfig(
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tokenizer_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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group_size=16,
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max_token_length=1024 * 16,
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complexity_mode=None, # Use default parameters
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dump_rollouts=True,
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)
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```
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#### Random Complexity Training
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```python
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env_config = ReasoningGymEnvConfig(
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tokenizer_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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group_size=16,
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max_token_length=1024 * 16,
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complexity_mode="random", # Randomize difficulty
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dump_rollouts=True,
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debug_logging=True,
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)
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```
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#### Curriculum Learning
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```python
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env_config = ReasoningGymEnvConfig(
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tokenizer_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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group_size=16,
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max_token_length=1024 * 16,
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complexity_mode="curriculum", # Adaptive difficulty
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curriculum_target_accuracy=0.7, # Maintain 70% accuracy
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dump_rollouts=True,
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dump_failed_rollouts=True,
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rollout_save_score_threshold=0.7,
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debug_logging=True,
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)
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```
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@ -106,6 +185,74 @@ await env.setup()
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python reasoning_gym_environment.py
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```
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### Monitoring Curriculum Learning
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When using curriculum mode, the environment logs detailed statistics:
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```python
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# Get curriculum statistics
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stats = env.get_curriculum_stats()
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print(f"Total tasks tracked: {stats['total_tasks_tracked']}")
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print(f"Tasks with adjustments: {stats['tasks_with_adjustments']}")
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print(f"Average complexity: {stats['avg_complexity']:.2f}")
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```
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Curriculum metrics are automatically logged to wandb:
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- `curriculum/total_tasks_tracked`
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- `curriculum/tasks_with_adjustments`
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- `curriculum/avg_complexity`
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- `curriculum/avg_recent_accuracy`
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## Complexity Control Details
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### Parameter Mappings
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Each task has carefully crafted complexity parameter mappings based on examination of reasoning-gym source code:
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#### Example: Basic Arithmetic
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```python
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"basic_arithmetic": {
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"min_terms": int(2 + complexity_level * 4), # 2-6 terms
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"max_terms": int(2 + complexity_level * 4),
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"min_digits": int(1 + complexity_level * 3), # 1-4 digits
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"max_digits": int(1 + complexity_level * 3),
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"allow_parentheses": complexity_level > 0.3,
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"allow_negation": complexity_level > 0.5,
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}
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```
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#### Example: N-Queens
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```python
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"n_queens": {
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"n": int(4 + complexity_level * 8), # 4-12 board size
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"min_remove": int(1 + complexity_level * 6), # 1-7 pieces removed
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"max_remove": int(1 + complexity_level * 6),
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}
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```
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### Curriculum Algorithm
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The curriculum system uses the following logic:
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1. **Initialization**: All tasks start at 30% complexity
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2. **Tracking**: Each task maintains independent performance history (last 10 groups)
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3. **Adjustment Trigger**: Requires ≥3 groups before making adjustments
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4. **Target Accuracy**: Default 70%, configurable
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5. **Adjustment Logic**:
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- If accuracy > target + 5%: Increase complexity by 5%
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- If accuracy < target - 5%: Decrease complexity by 5%
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- Special fast-track for very high (>90%) or very low (<30%) accuracy
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6. **Stability**: Considers performance variance to avoid erratic adjustments
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### Complexity Ranges
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All parameter ranges are based on actual reasoning-gym defaults with reasonable variations:
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- **Integer parameters**: Properly converted with `int()`
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- **Float parameters**: Only used where appropriate (e.g., edge probabilities)
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- **Boolean parameters**: Threshold-based activation
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- **Reasonable bounds**: No extreme values that would break tasks
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## System Prompt
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The environment uses a structured reasoning prompt that encourages models to:
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@ -154,28 +301,46 @@ Saved to `data_dumps/reasoning_gym_environment_FAILED_rollouts_{uuid}_{batch}.js
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The environment provides comprehensive logging:
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### Standard Logging
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- **Setup**: Task discovery and initialization
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- **Training**: Group scores, task selection, progress tracking
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- **Data Dumping**: Save progress and file creation
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- **Format Violations**: When models don't follow answer tag requirements
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- **Debug Mode**: Detailed scoring and extraction information
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### Curriculum Logging
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- **Complexity Adjustments**: Real-time difficulty changes per task
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- **Performance Tracking**: Accuracy trends and stability metrics
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- **Target Achievement**: When tasks reach optimal difficulty zones
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### Debug Mode
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Enable with `debug_logging=True` for detailed information:
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- Answer extraction attempts
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- Scoring method comparisons
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- Format violation details
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- Task selection patterns
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- Complexity parameter usage
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## Task Examples
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### Mathematics
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- **GSM Symbolic**: Grade school math with symbolic reasoning
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- **Basic Arithmetic**: Addition, subtraction, multiplication, division
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- **Basic Arithmetic**: Addition, subtraction, multiplication, division with configurable complexity
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- **Algebra**: Linear equations and polynomial manipulation
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### Logic
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- **Sudoku**: Classic number placement puzzles
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- **Propositional Logic**: Boolean reasoning tasks
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- **Knights and Knaves**: Logic puzzles with truth-tellers and liars
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- **Sudoku**: Classic number placement puzzles with variable difficulty
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- **Propositional Logic**: Boolean reasoning tasks with adjustable clause counts
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- **Knights and Knaves**: Logic puzzles with configurable people and statements
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### Programming
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- **ARC**: Abstract reasoning corpus visual patterns
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- **Code Generation**: Simple programming challenges
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- **Algorithm Design**: Sorting, searching, and optimization
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- **Algorithm Design**: Sorting, searching, and optimization with scalable complexity
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### Games
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- **N-Queens**: Chess queen placement with variable board sizes
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- **Tower of Hanoi**: Disk movement puzzles with adjustable disk counts
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- **Rush Hour**: Traffic jam puzzles with configurable car counts
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## Troubleshooting
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@ -185,6 +350,7 @@ The environment provides comprehensive logging:
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2. **Import errors**: Check that requirements.txt dependencies are installed
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3. **No rollouts saved**: Verify `dump_rollouts=True` and scores exceed threshold
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4. **Format violations**: Models not using `<answer>` tags receive 0 scores
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5. **Curriculum not adjusting**: Ensure tasks get enough groups (≥3) for adjustments
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### Debug Mode
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@ -198,19 +364,72 @@ This shows:
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- Scoring method comparisons
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- Format violation details
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- Task selection patterns
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- Complexity parameter mappings
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- Curriculum adjustment decisions
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## Performance Notes
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### Curriculum Monitoring
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- **Task Selection**: Random selection ensures diverse training
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- **Evaluation**: Fixed test set with deterministic seed for reproducible results
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- **Memory Usage**: Buffers are cleared after saving to prevent memory leaks
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- **Scoring Efficiency**: Dual-format scoring tries both methods and uses higher score
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Monitor curriculum effectiveness:
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```python
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# Check curriculum statistics
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stats = env.get_curriculum_stats()
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for task, details in stats['task_details'].items():
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if details['adjustable']:
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print(f"{task}: complexity={details['complexity']:.2f}, "
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f"accuracy={details['recent_accuracy']:.2f}")
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```
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## Contributing
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## Performance Considerations
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When adding new features:
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### Complexity Modes
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- **None**: Fastest, no overhead
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- **Random**: Minimal overhead, good for exploration
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- **Curriculum**: Slight overhead for tracking, optimal for learning
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1. Maintain backward compatibility with existing configs
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2. Add appropriate logging for debugging
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3. Update this README with new configuration options
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4. Test with both successful and failed rollout scenarios
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### Memory Usage
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- Curriculum mode stores performance history (last 10 groups per task)
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- Typical memory overhead: <1MB for all 102 tasks
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### Convergence
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- Curriculum typically converges to target accuracy within 50-100 groups per task
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- Fast-track adjustments help with extreme performance cases
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- Stability detection prevents oscillation around target
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## Advanced Usage
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### Custom Complexity Mappings
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To add complexity control for new tasks:
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```python
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def _get_complexity_params_for_task(self, task_name: str, complexity_level: float):
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# Add your custom task mapping
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if task_name == "my_custom_task":
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return {
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"difficulty": int(1 + complexity_level * 9), # 1-10
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"size": int(5 + complexity_level * 15), # 5-20
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}
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# ... existing mappings
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```
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### Curriculum Customization
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Adjust curriculum parameters:
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```python
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# More aggressive curriculum
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env_config.curriculum_target_accuracy = 0.8 # Higher target
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# In _adjust_task_complexity, modify:
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adjustment_threshold = 0.03 # Smaller threshold for more frequent adjustments
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complexity_step = 0.1 # Larger steps for faster adaptation
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```
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### Integration with External Systems
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The environment supports integration with external curriculum systems:
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```python
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# Override complexity for specific tasks
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env.task_complexity_levels["basic_arithmetic"] = 0.8 # Set to 80% complexity
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env.task_complexity_levels["n_queens"] = 0.3 # Set to 30% complexity
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```
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