TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
Score
81提出新型信用分配方法，解决长程Agent训练关键难题，性能提升显著，方法新颖简洁。
方法设计清晰，实验设置规范，结果对比完整，但缺乏更大模型和更多任务验证。
无需额外标注或复杂架构，可直接应用于现有RL训练流程，提升多轮Agent能力。
正值Agent和RL后训练研究热点，论文提交日期为2026年7月，内容前沿。
arXiv预印本，作者来自知名机构，但尚未经同行评审，可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Proposes a novel dense credit assignment method with significant experimental results and simplicity, but only validated on limited model scales, requiring more generalization tests.
AI commentary
「This paper proposes TRACE, an elegant credit assignment method that constructs state values from log-probability ratios, avoiding extra critics. The experimental gains are striking. For teams working on agent RL training, especially with long trajectories, this is a must-read.」
Quick overview
Long-horizon multi-turn agents require extensive tool calls, making outcome rewards sparse, high-variance, and misleading. This paper proposes TRACE, a dense credit assignment method that converts gold-answer log-probabilities from a frozen reference model into state values and derives per-action rewards via temporal-difference changes, requiring no critic or process labels. On BrowseComp-Plus, TRACE boosts Qwen3-4B from 7.2 to 35.6 and Qwen3-30B-A3B from 8.4 to 42.6, using pure RL without cold-start SFT or web data, with earlier improvement and faster convergence.
- TRACE builds state values from gold-answer log-probabilities of a frozen reference model and derives per-action rewards via TD learning, without extra critic or process labels.
- On the closed-web BrowseComp-Plus benchmark, TRACE raises Qwen3-4B from 7.2 to 35.6 and Qwen3-30B-A3B from 8.4 to 42.6.
- TRACE uses pure RL with no cold-start SFT or live-web data; learning curves show earlier improvement and faster convergence.
- Learned search behavior transfers to open-web benchmarks, demonstrating generalization.
Full text
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