ARES Documentation

ARES (Agentic Research and Evaluation Suite) is an RL-first framework for training and evaluating code agents.

Unlike traditional frameworks that treat the entire code agent as the optimization target, ARES enables reinforcement learning on the LLM within the agent. This provides fine-grained control over long-horizon tasks and opens up new possibilities for mechanistic interpretability research.

Contents:

• Core Concepts
  • System Architecture
  • Environment
  • CodeAgent
  • Container
  • LLMClient
  • Next Steps
• How It Works
  • Queue-Mediated Communication
  • Multiple Environments
  • Limitations and Trade-offs
  • Further Reading

Getting Started

See the main README for installation instructions and quick start examples.

Key Features

• RL-First Design: Built around the reinforcement learning loop with observations (LLM requests) and actions (LLM responses)

• LLM-Level Optimization: Train the LLM within code agents, not just the agent as a whole

• Distributed Workloads: Support for high-volume, distributed training and evaluation

• Mechanistic Interpretability: Raw access to LLM requests and responses for deep analysis

• Async Gym/dm_env like Spec: Close to Gym/dm_env spec, but incorporating async methods for performance

Indices and tables

• Index

• Module Index

• Search Page