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ClawLoop: Self-Improving Agents via Harness-Native Training Loop
Not provided in the abstract
self-improvementtask synthesisreinforcement learning
2610.14118
Builder Relevance
2h ago80%
Abstract
ClawLoop introduces a self-improving framework for LLM-based agents that operates without external teacher models, enabling continuous task synthesis and agent training.
Reality Card
Core Claim
ClawLoop enables self-improvement of LLM-based agents without external teacher models, resulting in consistent performance gains across multiple benchmarks.
Method / Result
ClawLoop yields consistent improvements on SkillsBench, Claw-Eval-Live, and ClawGym-Bench.
Limitations
The framework's reliance on the initial loop model may limit generalizability to other models.
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