ARES How It Works

This page summarizes the main implementation ideas that make ARES’s RL abstraction work: the queue-mediated LLM client, parallel environments, and the trade-offs involved.

Queue-mediated communication

The central pattern is the queue-mediated LLM client. It’s what turns “the agent calls the LLM” into “the environment gets an observation and you provide an action.”

The problem: You want an RL environment where (1) code agents are written in normal, linear code (reason → execute → repeat), (2) LLM interactions show up as observations and actions, and (3) the agent doesn’t know it’s in an RL loop.

How it works: The code agent uses a client that implements the usual “send request, get response” interface. But instead of calling an API, that client:

  1. Puts the request into an async queue and waits on a Future.
  2. The environment watches the queue. When a request appears, it returns that request as the current observation from reset() or step().
  3. Your policy produces an LLMResponse and passes it to env.step(action). The environment sets the Future’s result to that response.
  4. The agent’s await llm_client(request) then completes and it continues with your response.

So from the agent’s point of view it’s just “call LLM, get reply.” From the environment’s point of view, each of those calls is one observation–action step. The agent appears to block but is actually yielding control to the environment until you provide the next action.

Multiple environments

Environments are async and independent. Each has its own container, code agent, and queue. You can run many episodes in parallel, e.g. with asyncio.gather(), for distributed data collection. That scales to many parallel environments for training.

Limitations and trade-offs

  • Not a fit for: Sub-millisecond latency requirements (the queue adds overhead), or fully synchronous agent code (ARES assumes async/await).
  • Design trade-offs: Both the agent and the environment use async; the “blocking” LLM call is actually yielding, which can be surprising when debugging.

Full documentation

For more detail (including code snippets and the full dm_env-style interface), see Read the Docs:

How It Works on Read the Docs