EvoHarness-RL boosts AI agent tool use efficiency to 96.9%
bendee983 · x · 2026-08-29
Meta AI and the University of Illinois unveiled EvoHarness-RL, a framework designed to help AI agents optimally use external tools and harnesses.
Key BPE Interface Components:
- Belief: Maintaining an accurate read on the current environment.
- Progress: Managing completed and pending subgoals.
- Experience: Reusing historical knowledge across tasks.
This interface simplifies interaction with tooling, avoiding low-level API handling. The training pipeline includes:
- Supervised Fine-tuning: Learning to extract and structure facts from messy logs.
- Cost-aware RL: Teaching the model to use the harness efficiently with cost awareness.
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