新闻
DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- DRACO improves long-horizon agent training by dynamically generating rubrics during training and redistributing trajectory-level scores to individual steps for better credit assignment.
- 为何重要
- Matters for engineers building reinforcement learning agents on tasks without programmatic success checkers, where multi-criteria evaluation is the only feedback available.
- 注意
- Results shown on specific benchmarks like AppWorld and Tau-Bench; generalization to other long-horizon domains and real-world applicability remain unclear from the abstract.
- agent
- reinforcement learning
- long-horizon
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