Frozen LLM Agents Learn Domain via Harness Policy Optimization
A recent study available on arXiv (ID: 2607.25415) introduces an innovative technique for enhancing static large language models (LLMs) to achieve specialized knowledge. The approach revolves around a simplified framework, known as the harness, which integrates a prompt template, a suite of tools, a memory and retrieval mechanism, a planning approach, and a verification method. In contrast to upcoming systems like Meta-Harness and HyperAgents, both expected in 2026, this new strategy employs an epsilon-greedy contextual bandit alongside the REINFORCE learning algorithm. Its assessment is designed to ensure auditability while maintaining compatibility with proprietary model APIs.
Key facts
- Paper arXiv:2607.25415 proposes frozen LLM agent learning via harness optimization.
- Harness includes prompt template, tool set, memory/retrieval layer, planning strategy, verification policy.
- Two 2026 systems: Meta-Harness (Lee et al., 2026) and HyperAgents (Meta AI, 2026).
- Proposed method uses epsilon-greedy contextual bandit and REINFORCE.
- Multi-objective reward: task success, verifier score, policy compliance, cost, latency, unsupported-claim penalty.
- Approach is auditable and works with black-box model APIs.
- Avoids expensive code-search loops and unconstrained self-modifying code.
- Published on arXiv with ID 2607.25415.
Entities
Artists
- Lee et al.
Institutions
- Meta AI
- arXiv