arXiv Machine Learning July 16, 2026 By Leitian Tao, Baolin Peng, Wenlin Yao, Tao Ge, Hao Cheng, Mike Hang Wang, Jianfeng Gao, Sharon Li

TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

Read the original on arXiv Machine Learning →

arXiv:2607. 13988v1 Announce Type: new Abstract: Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 6

ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment

arXiv:2608. 05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer.

By Yijun Lu, Rui Ye, Jiajun Wang, Yuwen Du, Tian Jin, Songhua Liu, Siheng Chen

agents reinforcement-learning fine-tuning

arXiv AI

1d ago

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.

By Huan Zhang, Mingju Chen, Dongxu Zhou, Can Lv, Heng Chang, Sen Cui, Faguo Wu, Shiji Zhou

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 28

SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents

arXiv:2607. 23263v1 Announce Type: new Abstract: Deciding whether a trajectory actually fulfills its instruction governs how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning.

By Yang Wan, Zhenhao Zhang, Jierui Wang, Linchao Zhu

agents reinforcement-learning efficiency

arXiv AI

Jun 10

3SPO: State-Score-Supervised Policy Optimization for LLM Agents

arXiv:2606. 09961v1 Announce Type: cross Abstract: Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks.

By Yu Han, Kailing Li, Yang Jiao, Yulin Dai, Yuqian Fu, Linhai Zhuo, Tianwen Qian

llms agents reinforcement-learning

arXiv AI

Jun 30

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

By Yuxuan Jiang, Francis Ferraro

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 1

TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning

arXiv:2606. 32017v1 Announce Type: cross Abstract: Agentic reinforcement learning requires assigning credit to environment-facing actions such as searches, clicks, edits, navigation commands, and object interactions.

By Yuanda Xu, Zhengze Zhou, Hejian Sang, Xiaomin Li, Jiaxin Zhang, Xinchen Du, Zhipeng Wang, Alborz Geramifard

agents reinforcement-learning

Related stories

ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents

3SPO: State-Score-Supervised Policy Optimization for LLM Agents

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning