Recurrent Harness for Agentic Continual Learning
ReHarness revises retrieved agent reflections after each task, improving continual learning across three benchmarks and three backbones.
Researchers propose ReHarness, a recurrent harness for agentic continual learning that adds backward revision to forward read and write under a strictly online streaming protocol. After each instance, the resulting trajectory is used to revise reflections retrieved for that instance, propagating feedback through the textual harness state. Across three continual-learning benchmarks and three language-model backbones, ReHarness outperforms raw-context, memory- and reflection-based, and prompt-optimization baselines. Analysis finds the revisions are source-grounded and correct overgeneralized or outdated experience under distribution shift.
- ReHarness revises retrieved reflections using each instance's trajectory as feedback.
- It follows a strictly online streaming protocol without replay or validation data.
- It beats raw-context, memory, reflection, and prompt-optimization baselines on three benchmarks.
- Revisions correct outdated experience and improve both stability and plasticity.
From the original article
1 min read at huggingface.co
Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harness optimization.
By Yuxin Xiao, Zhiyun Lu, Jianyu Wang · huggingface.co
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