AI RESEARCH
PACE: Two-Timescale Self-Evolution for Small Language Model Agents
arXiv CS.LG
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ArXi:2605.23019v1 Announce Type: new Deploying language-model agents in production often requires substantial compute and human effort to tune prompts, parsers, validators, and other components of the agent pipeline. Self-evolution offers a promising alternative, but most existing frameworks assume access to frontier models that can reliably diagnose failures, propose revisions, and judge their own updates. We study whether frozen small language models (SLMs) can serve as effective self-evolving agents under resource constraints.