RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

Problem

The paper addresses the issue of overfitting in the recursive self-improvement of agent harnesses, which can lead to suboptimal performance in both in-distribution and out-of-distribution scenarios. This work is presented as a preprint and has not undergone peer review.

Method

The authors propose Regularized Recursive Self-Improvement (RRSI), which integrates regularization principles into the self-improvement process of agent harnesses. The RRSI framework consists of two main components:

• Proposer: This component operates with a temporally annealed budget, which limits the number of edits made to each candidate solution. It encourages exploration of previously unexplored trajectories based on the evolution history of the agent harness.
• Selector: The selector is equipped with a critic and a pruner. The critic evaluates benchmark-specific proposals to ensure they meet performance criteria, while the pruner eliminates changes that are deemed too small, too costly, or no longer beneficial to the improvement process.

Results

The results indicate significant improvements in performance metrics:

• In-distribution gain: Up to 14.1 points compared to a baseline (specific baseline not disclosed).
• Out-of-distribution gain: Up to 4.7 points compared to a baseline (specific baseline not disclosed).
• The RRSI method utilized 30% fewer policy tokens than the unregularized evolution approach, indicating a more efficient use of resources during the self-improvement process.

Limitations

The authors acknowledge the potential for overfitting, which remains a concern despite the proposed regularization techniques. Additionally, the evaluation is heavily reliant on specific benchmarks, which may limit the generalizability of the findings to other contexts or applications.

Why it matters

The introduction of RRSI has significant implications for the development of more robust agent harnesses capable of self-improvement without succumbing to overfitting. This work could pave the way for future research focused on enhancing the efficiency and effectiveness of agent learning systems, particularly in dynamic environments where adaptability is crucial.