hud-vf-gym 0.1.0

HUD environments for Reinforcement Learning through Verifiers

hud-vf-gym

Verifiers Adapter for HUD environments - bridges Verifiers RL framework with HUD's MCP infrastructure for training and evaluating agents.

Training Support

Verifiers' GRPOTrainer does not support multimodal training as of now, you can use an [experimental trainer]https://github.com/jdchawla29/verifiers) for single-turn environments (with single prompt image due to transformer's limitations). Multi-turn multimodal support is WIP.

Prerequisites

• Python >=3.12
• HUD API key from https://app.hud.so
• Environment variables:

  export HUD_API_KEY="your-api-key"
  export OPENAI_API_KEY="your-key"  # or ANTHROPIC_API_KEY
  

Quick Start

import verifiers as vf

# Load environment with HUD taskset and config
env = vf.load_environment(
    env_id="hud-vf-gym",
    taskset="hud-evals/2048-taskset",  # HuggingFace dataset
    config_path="./configs/2048.yaml",  # Environment config
    num_tasks=10
)

Documentation

For comprehensive usage, examples, and configuration details, see: hud-python/rl README

The main documentation covers:

• Running evaluations with various models
• Training agents with GRPO
• Creating custom environments and configs
• Dataset format and creation
• Action mappings and tool configuration
• Troubleshooting guide

License

MIT