arXiv AI July 31, 2026 By Naifan Zhang, Ruihan Sun, Jinwei Su, Hengjie Yang, Zhengyuan Pan, Zhaohan Chen, Xiaofan Zhang

MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue

arXiv:2603. 06194v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn interaction remains challenging due to sparse rewards and poor per-turn credit assignment.

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

arXiv Machine Learning

Jun 16

EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management

arXiv:2606. 15532v1 Announce Type: cross Abstract: Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation.

By Rongzhi Zhu, Xiang Huang, Yuchuan Wu, Rui Wang, Zequn Sun, Tao Ren, Weiyao Luo, Bingxue Qiu, Jieping Ye, Yongbin Li, Wei Hu

llms reinforcement-learning benchmarks

arXiv AI

Jun 10

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.

By Heming Zou, Qi Wang, Yun Qu, Yuhang Jiang, Lizhou Cai, Yixiu Mao, Ru Peng, Xin Xu, Weijie Liu, Kai Yang, Saiyong Yang, Xiangyang Ji

llms agents reinforcement-learning benchmarks

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 Machine Learning

Jul 16

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

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.

By Leitian Tao, Baolin Peng, Wenlin Yao, Tao Ge, Hao Cheng, Mike Hang Wang, Jianfeng Gao, Sharon Li

agents reinforcement-learning fine-tuning 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

arXiv AI

Aug 10

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents

arXiv:2608. 06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions.

By Hongxi Yan, Ziyue Huang, Shichao Fan, Qingjie Liu

llms agents multimodal

Related stories

EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

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

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

TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents