DemoEvolve: Demonstration-Bootstrapped Harness Evolution for Language Agents
A new AI research paper introduces DemoEvolve, a method for improving frozen language-model agents by evolving their external harness structures using human demonstrations. The approach addresses the problem of sparse rewards in long-horizon stochastic environments, where self-generated rollouts become fragile. DemoEvolve uses competent human trajectories as expert reference experience to guide harness-level diagnosis, overcoming the limitations of reward-only search.
Key facts
- DemoEvolve is a demonstration-bootstrapped approach to harness evolution.
- It improves frozen language-model agents by modifying executable structures around them.
- Prior work shows self-generated rollouts can support harness search.
- In long-horizon stochastic environments, self-practice becomes fragile due to sparse rewards and high variance.
- Human trajectories serve as expert reference experience for the coding proposer.
- The paper is published on arXiv with ID 2605.24539.
- The method is described as sample-efficient fast adaptation.
- Base model's general capabilities remain intact while task-specific competence is acquired.
Entities
Institutions
- arXiv