arXiv cs.CL· Alexander Gill, Guram Gogia, Francisco Guzm\'an·· 3 天前AI 评分31
Precog-RM：预测 LLM 智能体轨迹结果的学习表示
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
AI 导读
研究者将智能体轨迹重新定义为动作与环境观测共享嵌入空间中的路径，发现同一任务的成功与失败 rollout 在该空间中可分离，简单判别器在三个智能体基准和三个策略模型上预测结果 AUROC 最高达 0.95。
正文 · 原文
Abstract:In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations. We show that successful and failed rollouts of the same task separate in this space, with simple per-task discriminators predicting held-out rollout outcomes at up to 0.95 AUROC across three agent benchmarks and three policy models. To exploit this signal across tasks, we introduce Precog-RM, a transformer-based model that meta-learns a representation in which outcome prediction can be performed in a few-shot setting. We show that Precog-RM generalizes to unseen tasks and models, and gives confident predictions of trajectory success long before termination. Deployed as an early stopping controller for Group Relative Policy Optimization (GRPO) on Tau2-Bench, Precog-RM cuts training FLOPs by up to 47.3\% while maintaining performance comparable to baseline GRPO.
| Comments: | 9 pages, 8 pages appendix |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19614 [cs.LG] |
| (or arXiv:2610.19614v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19614 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alexander Gill [view email]
[v1]
Wed, 14 Oct 2026 05:36:22 UTC (377 KB)
来源：arXiv cs.CL · arxiv.org