Meta and MIT's ReHarness Rewrites Agent Reflections Using Task Outcomes
dair_ai · x · 2026-10-13
A Meta/MIT paper proposes ReHarness, a recurrent harness for agentic continual learning. Existing memory harnesses only write forward—lessons are never checked against outcomes. ReHarness uses each finished trajectory as feedback to revise the reflections that guided it, under a strictly online protocol. Across three continual-learning benchmarks (16 tasks) and three backbones, it beats raw-context, memory, reflection, and prompt-optimization baselines, with gains significant after multiple-comparison correction.
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