arXiv Machine Learning November 2, 2026 By Zhenchao Sun, Chongyang Tao, Ping Lu, Tongkai Lu, Jianling Gao, Shuai Ma

SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences

Read the original on arXiv Machine Learning →

SAT-ACT introduces an outcome‑derived learning framework for improving branching decisions in conflict‑driven clause learning (CDCL) SAT solvers. It extracts action preferences from independent branching rollouts using three outcome comparison criteria, allowing the model to learn branching priorities without forcing a predefined ordering. The approach also incorporates non‑dominated heuristic action supervision, and experimental results show consistent reductions in CDCL search costs and improved PAR‑2 performance on SAT Competition benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Oct 12

Matched-Context Group Policy Optimization for LLM Agent Training

Matched-Context Group Policy Optimization (MCGPO) is a method for training long-horizon language model agents by constructing weighted action preferences from pre-collected trajectories. It defines comparison contexts using pre-action state summaries and rubric progress, and employs two-action support with exact and coarse matching rules to weight evidence for learning. Experiments across six environment-backbone settings show that MCGPO improves mean success rates over matched GraphGPO, achieving up to 5.16 percentage point gains on WebShop with Qwen3-4B and 8B models.

By Chenxu Zhang, Yaorui Shi, Wenlong Zhang, Jianchao Tan, Zihan Wang, Yiming Cheng

llms agents

arXiv AI

Jul 7

LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL

arXiv:2607. 04412v1 Announce Type: new Abstract: Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals.

By Yujin Kim, Namgyu Ho, Sangmin Hwang, Joonkee Kim, Yongjin Yang, Sangmin Bae, Seungone Kim, Jaehun Jung, Se-Young Yun, Hwanjun Song

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

Aug 5

Agentic Reinforcement Learning with Self-Distilled Reward Shaping

arXiv:2608. 03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions deserve credit.

By Ranxu Zhang, Guinan Chen, Chenshaodong, Jinghao Lin, Xiaozhou Xu, Sunzhe, Yanyong Zhang, Chao Wang

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 10

Beyond Success Rates: Trainability and Extractability for Offline GCRL

arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.

By Jan Malte T\"opperwien, Aditya Mohan, Marius Lindauer

reinforcement-learning robotics

arXiv AI

Jul 20

Process Reward Informed Tree Rollout for Effective Multi-Turn RL

arXiv:2607. 15610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories for advantage estimation.

By Xintong Li, Sha Li, Yuwei Zhang, Changlong Yu, Rongmei Lin, Hongye Jin, Shuyi Guan, Xin Liu, Linwei Li, Qingyu Yin, Jingbo Shang

llms agents reinforcement-learning

One email a morning, machine-written

Related stories

Matched-Context Group Policy Optimization for LLM Agent Training

LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL

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

Agentic Reinforcement Learning with Self-Distilled Reward Shaping

Beyond Success Rates: Trainability and Extractability for Offline GCRL

Process Reward Informed Tree Rollout for Effective Multi-Turn RL