Dependency-Aware Trajectory Refinement for Efficient Multi-Turn Agent Fine-Tuning

2026-09-16

AI Summary

该研究针对多轮代理轨迹中的冗余问题，通过构建依赖DAG来精简轨迹，并基于此进行微调，以提高效率和准确性。

Abstract

Multi-turn agent trajectories often contain redundant rounds (failed tool calls, parallel sub-queries, verification-only steps) that inflate both training and inference cost. We propose viewing each trajectory as a \emph{round-level dependency DAG} that exposes which rounds are globally load-bearing for the final answer, and fine-tune agents on trajectories refined through this DAG. Given an LLM-annotated DAG, these edits are deterministic and interpretable, with optional rephrasing. Models trained on these refined trajectories consistently outperform those trained on the original trajectories at lower inference cost. Specifically, across four multi-modal QA benchmarks, our refinements improve downstream accuracy by up to $1.7$\,pp over vanilla SFT (and $5.7$\,pp over an LLM-deletion baseline) while reducing per-sample inference messages by up to approximately $40\%$ and inference tokens by up to approximately $48\%$, translating to substantial savings in compute and serving cost. Code is available.

Problem

• multi-turn agent trajectories
• redundant rounds
• training and inference cost

Innovation

• dependency-aware trajectory refinement
• round-level dependency DAG
• efficient fine-tuning
• reducing inference cost