Process Supervision

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9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision
Oct 7, 2026Wenxi GanLearning from Demonstration Agent Skill Learning
City University of Hong Kong Shenzhen Loop Area Institute
Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Oct 7, 2026Taehoon Kim, Seunggeun Cho, Dongsu HanReasoning Distillation LLM Reasoning
KAIST
HaPRL: Human-Anchored Process Reinforcement Learning for Visual Search Agent
Sep 29, 2026Zhangquan Chen, Yaoxin Niu, Xiang An +5Human-in-the-Loop Annotation Process Supervision
Tsinghua University · Peng Cheng Laboratory · LMMs-Lab +2
Rewarding Novel Deductions: Solver-guided Process Supervision for Logical Reasoning
Sep 28, 2026Muhammad Asif Ali, Wenqing Wang, Huan Wang +1RL for Language Model Reasoning Process Supervision
FORTE Lab, Faculty of Science, Information Technology University, Lahore, Pakistan · College of Informatics, Huazhong Agricultural University, Wuhan, China · Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
Reinforcement Learning from Intermediate Renders for Image-to-Code Generation
Sep 28, 2026Omri Kaduri, Kate Feingold, Phillip Isola +1RL for Code Generation Code Generation
Weizmann Institute of Science · MIT
Combining Hierarchical Cognitive Process with Process Supervision for Interpretable Scene Safety Understanding
Sep 22, 2026Zhiyun Jiang, Hanyong Wang, Binbin Liang +4LLM Interpretability Process Supervision
FLARE: A Full-Lifecycle Dense Supervision Paradigm for Long-Horizon Coding Agents via Generative Reward Model
Sep 20, 2026Jingxuan Xu, Gang Wu, Yanan Wu +13RL for Code Generation Reward Modeling
Independent Researcher · Nanjing University · Peking University +1
Debate-to-Skill: Capability-Bound Process Supervision for Industrial Query-to-Agent Annotation
Sep 12, 2026Shiyu Zhang, Leisheng Cheng, Huifu LiLLM-Assisted Annotation Process Supervision
Baidu, Inc.
Structural Process Supervision for Latent Chain-of-Thought Reasoning
Sep 9, 2026Yiqi Li, Xu Chen, Chen Ju +5Representation Learning Process Supervision
Shanghai Jiao Tong University · Taobao & Tmall Group
IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
Sep 8, 2026Liang Cao, Weide Liu, Yan Qin +3Predictive Process Monitoring Process Supervision
Department of Chemical and Biological Engineering University of British Columbia Vancouver, BC, Canada · School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang, China · School of Automation Chongqing University Chongqing, China +2
SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents
Sep 7, 2026Chenyu Zhou, Qiliang Jiang, Shuning Wu +1Constrained RL Runtime Enforcement for AI Agents
School of Engineering, Institute of Science Tokyo, Japan · College of Control Science and Engineering, Zhejiang University, China · Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Cliff: Learning Process Rewards from the First Mistake
Sep 2, 2026Peixuan Han, Runhui Wang, Ketan Ramaneti +3Reinforcement Learning with Verifiable Rewards LLM Reasoning
1Amazon Web Services · University of Illinois Urbana-Champaign
Monitoring Web Agents Without Internal Signals: Observable Trajectories and Key-Step Supervision
Sep 2, 2026Sitong Pan, Yipeng Shen, Yilin Lu +3AI Agent Monitoring Process Supervision
University of Minnesota · 2Purdue University · 3The Pennsylvania State University
Dense Process Supervision for Search Agents via Fact Utility Estimation
Sep 1, 2026Rongzhi Zhu, Xiangyu Liu, Yi Liu +7Reinforcement Learning Credit Assignment in RL
State Key Laboratory for Novel Software Technology, Nanjing University, China · Ant Group, China · National Institute of Healthcare Data Science, Nanjing University, China
From Final Artifacts to Trajectories: Retrospective Process Supervision for Evidence-Grounded Long-Form Generation
Aug 31, 2026Junjie Huang, Jiarui Qin, Di Yin +4LLM Agent Trajectory Synthesis Process Supervision
Shanghai Jiao Tong University · Tencent Youtu Lab
Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence
Aug 8, 2026Ling Lin, Yang Bai, Congcong Zhu +6Visual Spatial Reasoning Inference-Time Search
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230000, China · Suzhou Institute for Advanced Research, USTC, Suzhou 215123, China · Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Singapore +4
CausalOPD: First-Wrong-Step Supervision for Distilling Causal Chain Reasoning
Aug 4, 2026Jian Zhang, Bingyi Wang, Yizhi LiuCurriculum RL Causal Reasoning in Language Models
Zhejiang University
PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation
Aug 4, 2026Yongshi Ye, Biao Fu, Chongxuan Huang +2RL for Language Model Reasoning Process Supervision
Institute of Artificial Intelligence, Xiamen University · Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism · School of Informatics, Xiamen University
Scaling Scientific Discovery Environments for Turn-Level Agentic RL
Jul 31, 2026Yucheng Xu, Keyi Zhang, Yuyang Yu +4Process Supervision Large Language Model-Guided Scientific Discovery
Shanghai AI Lab
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use
Jul 2, 2026Jiayin Zhu, Kelong Mao, Yudong Guo +4LLM Agent Evaluation LLM Agent Skill Learning
1HKUST(GZ), 2JD.COM · ∗Yutao Yue is also affiliated with Institute of Deep Perception Technology, JITRI, Wuxi, China.
FaithMed: Training LLMs For Faithful Evidence-Based Medical Reasoning
Jul 1, 2026Zhiyun Zhang, Liwen Sun, Xiang Qian +1RL for Language Model Reasoning Process Supervision
Carnegie Mellon University · Stanford University School of Medicine · Xlue
GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision
Jul 1, 2026Dianyu Wang, Peirong Zhang, Xuyang Li +2Vision-Language Grounding VLM Reasoning
Aerospace Information Research Institute, the Key Laboratory of Network Information System Technology (NIST), and the Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
Do Models Read What They Write? Causal Registers in Scratchpad Reasoning
Jun 28, 2026Benjamin Shih, John Winnicki, Eric DarveCoT Faithfulness CoT Reasoning
Institute for Computational and Mathematical Engineering Stanford University
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Jun 25, 2026Aoyang Fang, Yifan Yang, Jin'ao Shang +7LLM Agent Evaluation Causal Attribution
The Chinese University of Hong Kong, Shenzhen · Lero · The Chinese University of Hong Kong
A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
Jun 24, 2026Sen Li, Haichao Cui, Chendong Shao +2Unsupervised Domain Adaptation Domain Adaptation
Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.
Process-Reward Tactic Evolution for Long-Horizon Bioinformatics Workflows
Jun 18, 2026Lingzhi Yang, Yubo Fan, Song Wu +1LLM Agent Skill Learning Process Supervision
Stony Brook University · Vanderbilt University · Brookhaven National Lab
Topological Data Analysis for High-Dimensional Dynamic Process Monitoring
Jun 18, 2026Angan Mukherjee, Tyler A. Soderstrom, Michael J. Kurtz +1Process Supervision Topological Representation Learning
Department of Chemical & Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA · ExxonMobil Technology and Engineering, 22777 Springwoods Village Pkwy, Spring, TX 77389, USA
What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
Jun 18, 2026Xinghao Chen, Chak Tou Leong, Wenjin Guo +3Continuous Latent Reasoning Process Supervision
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
GraphPO: Graph-based Policy Optimization for Reasoning Models
Jun 17, 2026Yuliang Zhan, Xinyu Tang, Jian Li +7Reinforcement Learning LLM Reasoning with Graphs
Gaoling School of Artificial Intelligence, Renmin University of China · Ant Group
Video-Based Prediction of In-Flight Particle Characteristics in Atmospheric Plasma Spraying
Jun 5, 2026Abhijeet Praveen, Sareh Soleimani, Cormac Cureton +4Materials Science Video Prediction
Department of Electrical and Computer Engineering, McGill University, Montreal, QC H2X 2G6, Canada · Mila – Quebec AI Institute, Montreal, QC H2S 3H1, Canada · National Research Council of Canada, Boucherville, QC J4B 6G4, Canada

Oct 7, 2026, cs.CLJ/K move · Enter open · S save

Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision

Wenxi Gan

City University of Hong Kong Shenzhen Loop Area Institute

Learning from Demonstration Agent Skill Learning Knowledge Distillation Process Supervision

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Learning from large-model demonstrations offers a way to train small agents that can complete recurring tasks without calling a large model at every step. A central design choice is what to retain from teacher trajectories that contain reasoning, actions, and information about task progress. We introduce Task-Progress Distillation (TPD), an offline approach that pairs each demonstrated action with a short label describing the current task stage. The student learns these compact targets and selects actions by jointly scoring admissible stage--action pairs, which a deterministic harness executes in the environment. On ALFWorld, a 1.7B student trained with 404 demonstrations achieves 72.4% mean unseen task success with either TPD or action-only supervision, compared with 48.3% for a reasoning-trained student using constrained action selection. Explicit stages provide an additional benefit at 200 demonstrations, improving success from 48.0% to 67.7% over action-only supervision. With more demonstrations, the action-only student closes the gap, and both approaches reach 76.9% at 808 demonstrations. Shared-history analyses link part of TPD's local advantage to better decisions when moving between subgoals, particularly from object acquisition to processing. These results show that compact supervision can train effective small task agents, while explicit task progress provides additional guidance at an intermediate demonstration budget.

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