More Rules, Longer Lists? Benchmarking Self-Evolving Agents on a Hidden-Rule Curriculum

Not provided in the abstract

self-evolutionbenchmarkingmachine learningcurriculum learning

2610.40758

Builder Relevance

60%

1d ago

Abstract

This paper introduces CLR-Bench, a benchmark for evaluating self-evolving agents' ability to learn hidden rules through feedback.

Reality Card

Core Claim

The study demonstrates that self-evolving agents struggle to learn new rules without explicit feedback, achieving only marginal improvements over frozen agents.

Method / Result

The best self-evolving methods gained at most 5 points in accuracy compared to a frozen agent.

Limitations

The agents evaluated barely learn and show limited improvement, raising concerns about the effectiveness of self-evolution methods.

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