arXiv:2606.30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test-time computation. To separate these effects, we introduce a controlled student-teacher protocol across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1, evaluating thirteen open-weight models in both student and teacher roles. We compare external feedback, self-fee
The proliferation of language models and multi-agent systems necessitates a deeper understanding of how feedback truly drives improvement amidst rapid AI development cycles.
Understanding the efficacy of feedback versus other factors in AI agent performance is critical for optimizing training, deployment, and the development trajectory of autonomous systems.
This research provides a methodology to disentangle true learning from feedback in AI agents, moving beyond simple accuracy metrics to reveal underlying mechanisms of improvement.
- · AI researchers
- · AI platform developers
- · Companies deploying AI agents
- · Inefficient AI development practices
More efficient and targeted approaches to AI agent training and fine-tuning will emerge.
The development of more robust, truly adaptive AI agents capable of understanding and leveraging complex feedback will accelerate.
This could lead to a significant increase in the reliability and capability of autonomous AI systems across various applications, amplifying their impact on industries.
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Read at arXiv cs.AI