Harness-R1 dynamically edits agent harnesses via RL
Harness-R1 is an open-research reinforcement learning framework designed to train an exogenous meta-controller that edits the executable runtime environment of LLM agents. By dynamically modifying middleware lifecycle hooks—including initialization, pre-hint formatting, pre-action checks, and post-step feedback—Harness-R1 allows autonomous agents to automatically repair systematic failure patterns and boost task completion rates without full parameter retraining.
Adapting the agent harness rather than retraining base model weights is a powerful architectural shift that makes runtime error correction significantly faster and cheaper.
• Intervening at dynamic middleware lifecycle hooks enables targeted runtime fixes for recurring agent failure modes.
• Using an exogenous meta-controller keeps base model inference untainted while introducing strict execution safeguards.
• Cascading harness edits across multiple lifecycle hooks could increase system complexity and complicate root-cause debugging.
DISCOVERED
1d ago
2026-08-06
PUBLISHED
1d ago
2026-08-06
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