atropos/environments/infinimath/README.md
2025-05-12 07:26:10 +10:00

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# InfiniteMath Environment
## Environment Overview
This environment provides procedurally generated math problems with curriculum-based advancement. It allows an agent to solve increasingly difficult math problems, with the difficulty level adapting based on performance.
**Demonstrates:**
- Procedural content generation (math problems).
- Curriculum learning: The environment automatically adjusts the difficulty (levels 1-7) based on the LLM's success rate.
- Step-by-step reasoning evaluation: Rewards correctness, the presence of reasoning steps (within `<think>` tags), and the final answer format (`\boxed{}`).
- Handling LaTeX formatting for problems and answers.
**Training Goal:**
- To train LLMs to solve mathematical problems accurately.
- To encourage explicit step-by-step reasoning before providing an answer.
- To improve the LLM's ability to follow specific formatting instructions (using `<think>` tags and `\boxed{}`).
- To teach the model to handle progressively more complex problems through the curriculum.
## Features
- Progressive difficulty scaling across 7 levels of math problems
- Built-in curriculum system that adapts to agent performance
- Automatic problem generation with solutions
- Reward functions for accuracy, formatting, and boxed answer checking
## Usage
### Running with Default Configuration
To run the InfiniteMath environment with the default configuration:
```bash
python environments/infinite_math/infinimath_local_server.py
```
This will use the default configuration from `configs/envs/infinimath.yaml`.
### Custom Configuration
You can specify a custom configuration file:
```bash
python environments/infinite_math/infinimath_local_server.py --config my_custom_config
```
The `--config` parameter can be:
1. A name (without `.yaml` extension) which will be looked up in `configs/envs/`
2. A relative or absolute path to a YAML file
For example:
```bash
# Using a config in configs/envs/
python environments/infinite_math/infinimath_local_server.py --config infinimath_hard
# Using a config with full path
python environments/infinite_math/infinimath_local_server.py --config /path/to/my/config.yaml
```
## Configuration Structure
The configuration file follows this structure:
```yaml
# Base environment parameters
tokenizer_name: "NousResearch/DeepHermes-3-Llama-3-8B-Preview"
group_size: 1
use_wandb: false
# ... other base parameters
# InfiniteMath specific configuration
infinimath:
# Curriculum parameters
starting_level: 1
progress_threshold: 0.7
# ... other InfiniteMath specific parameters
# Server configuration
server_configs:
- model_name: "gpt-4.1-nano"
api_key: ${OPENAI_API_KEY}
num_requests_for_eval: 70
```
### Important Configuration Parameters
#### Base Parameters
- `tokenizer_name`: The tokenizer to use for encoding/decoding text
- `group_size`: Number of responses to collect per prompt
- `max_token_length`: Maximum token length for generation
- `steps_per_eval`: How often to run evaluations
#### InfiniteMath Specific Parameters
- `starting_level`: Initial difficulty level (1-7)
- `progress_threshold`: Success rate needed to advance levels
- `min_evaluations`: Minimum number of evaluations before level advancement
- `reward_functions`: List of reward functions to apply
#### Server Configuration
- `model_name`: LLM model to use
- `api_key`: API key for the model (can use environment variables with ${VAR_NAME} syntax)
- `num_requests_for_eval`: Number of evaluation requests to allocate