Agents
EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
arXiv:2609.35290v2 Announce Type: replace Abstract: Recent work has explored improving agents by jointly evolving their harnesses and models, but often takes a ''potpourri'' approach that bundles toge
arXiv:2609.35290v2 Announce Type: replace Abstract: Recent work has explored improving agents by jointly evolving their harnesses and models, but often takes a ''potpourri'' approach that bundles together new tools, new decision-making procedures, and model adaptation to the evolved harness under a single notion of agent improvement. In this paper, we instead investigate how agents can improve their decision-making procedures. In particular, we propose EvoIn, an agent fine-tuning framework that bridges evolution and internalization. EvoIn first analyzes agent execution traces to evolve and validate new decision-making procedures by temporarily instantiating them in the harness. The validated procedures guide the agent to generate improved reasoning traces. These traces are then rewritten into self-contained reasoning traces, removing explicit references to harness instructions while expressing the induced decision logic as the model's own reasoning. Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time. We evaluate EvoIn on diverse benchmarks and find that it consistently enables agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain. Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that agents can learn to decide how to solve a task before solving it, for example by checking a document's length to choose between reading it in full and searching it. EvoIn is also broadly applicable, showing consistent improvements on another model family.
Related
- VACE: Validation-Gated Alternating Co-Evolution of Agent Models and Harnesses
- EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses
- When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement
- VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
- SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
Source: arXiv cs.AI | 2026-10-28