Researchers from Meta AI and the University of Illinois Urbana-Champaign have published a new paper introducing EvoHarness-RL, a trainable coordination layer that lets large language model agents create, access, and manage their own external state. The result: a 96.9% success rate on a standard benchmark, up from 47.9% for the baseline model running without it.
What EvoHarness-RL actually does
LLM agents have limited context windows. When a task stretches across many steps, involving dynamic API connections, server logs, pending subgoals, and error recovery, the model’s internal memory isn’t enough. It needs an external scaffold to track what it believes about the world, what progress it’s made, and what it’s learned from past mistakes.
The framework introduces a structured external state built around three components: Belief, Progress, and Experience, collectively called BPE. Belief captures the agent’s current understanding of its environment. Progress tracks completed and pending subgoals so the agent doesn’t skip steps or duplicate work. Experience stores lessons from errors, like a database rejecting a batch due to API rate limits, so the agent can adapt its approach.
Training happens in two stages. First, supervised fine-tuning using expert demonstrations teaches the model how to interact with the harness. Then a cost-aware optimization technique called Group Relative Policy Optimization, or GRPO, refines the agent’s behavior to balance task performance against the computational cost of harness calls.
