Agent harnesses enable recursive self-improvement without model weight updates
Original title: Beneath the Surface: What We Overlook When We Talk About Agent Evolution
Evolved agents gain new tasks but lose covered ones, with reliability gains often raising deployment costs
We examine whether higher benchmark scores yield reliable, efficient, and transferable improvement for agent evolution without updating model weights. All sixteen evolved agents gain previously uncovered tasks while losing previously covered ones. The highest-scoring agent does not yield the largest mean improvement. Transfer selectively preserves gains on coding targets. These findings motivate preservation-aware evaluation, tested decision rules, and explicit applicability conditions as a constructive follow-up.
- All sixteen evolved agents gain previously uncovered tasks while losing previously covered ones
- Specific changes usually outperform generic reminders
- Stricter admission can discard useful alternatives
- Transfer selectively preserves gains on coding targets
From the original article
1 min read at huggingface.co
Agent harness evolution enables recursive self-improvement (RSI) without updating model weights. We examine whether higher benchmark scores yield reliable, efficient, and transferable improvement. We evaluate four methods across harnesses, general and coding tasks.
By Xiaobo Wang, Zimu Wang, Siyu Ye · huggingface.co
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