Reinforcement Learning

Also known as RL

Follow this topic Ask about this topic

Momentum

144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 1,138

All topics

Temporally Interpretable Differentiable Decision Trees
Oct 7, 2026Eisuke Hirota, Aarav Sane, Rohan PalejaInterpretable ML Interpretability in RL
Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California
RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning
Oct 7, 2026Yongqiang Yao, Jinru Tan, Kaihuan Liang +5RL for Language Models Reinforcement Learning
Shanghai Jiao Tong University · Central South University · SenseTime Research +2
Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient
Oct 7, 2026Lijun Bo, Yijie Huang, Chenhao LuRL Control Reinforcement Learning
CERO: Where and When to Allocate Rollouts for RL Post-Training
Oct 7, 2026Yiming Zong, Yige Wang, Xing Hu +2Reinforcement Learning RL Post-Training
Department of Industrial Engineering & Decision Analytics, Hong Kong University of Science and Technology · Innovation and Information Management, The University of Hong Kong · Faculty of Engineering & Faculty of Business and Economics, The University of Hong Kong
AGAR: a reinforcement learning substrate for LLM program evolution
Oct 6, 2026Haoran Li, Zengle Ge, Xiaomin Yuan +11LLM-Based Program Synthesis RL for Code Generation
University of the Chinese Academy of Sciences · Baidu · Tsinghua University +2
Convex-Concave Reinforcement Learning
Oct 6, 2026Shripad V. Deshmukh, Yaswanth Chittepu, Dhawal Gupta +2Reinforcement Learning Policy Gradient Methods
University of Massachusetts · Institute of Foundation Models
LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL
Oct 6, 2026Songyuan Zhang, Oswin So, Eric Yang Yu +3Reinforcement Learning Offline RL
MIT · MIT Lincoln Laboratory
Reinforcement Learning with Conformal Action Sets: An Application to Sequential Recommendation
Oct 6, 2026Wenwen Si, Honghao WeiSequential Recommendation Reinforcement Learning
University of Pennsylvania · Washington State University
Selective Transfer of RL Updates for Visual Reasoning
Oct 6, 2026Suxin Ji, Hungtao Wan, Mingjun Liu +1Reinforcement Learning VLM Reasoning
University of Pennsylvania · Independent Researcher · University of Electronic Science and Technology of China +1
Reinforcement Learning with Segment Reward Feedback under Linear Function Approximation
Oct 6, 2026Fengxu Liu, Siwei Wang, Gal Dalal +2Regret Minimization in RL Reinforcement Learning
National University of Singapore. · Microsoft Research Asia. · NVIDIA Research. +2
Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning
Oct 6, 2026SungJae Ahn, Jeong Woon Lee, Kyoleen Kwak +1RL Control Reinforcement Learning
College of Software, Kyung Hee University.
FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents
Oct 6, 2026Jia Liufu, Bin Hu, Linglin Jing +5RL for Code Generation Software Engineering Agents
NVIDIA · Tsinghua University
Learned Adaptive Multiresolution Diffusion Imaging
Oct 6, 2026Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås +1Reinforcement Learning Adaptive Mesh Refinement
Center for Integrative Petroleum Research, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia · Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway · Algorithmiq S.r.l., c/o STLex, Via della Chiusa 15, 20123, Milano, Italia
Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity
Oct 5, 2026Lan Shi, Daigo Shishika, Xuan WangReinforcement Learning Multi-Agent Reinforcement Learning
George Mason University, USA
Considering Context: When World Models Need Context Encoders
Oct 5, 2026Oleg Smirnov, Sofiane Ennadir, John Pertoft +2Reinforcement Learning World Model Learning
King AI Labs, Microsoft Gaming
GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement
Oct 5, 2026Pavlos Stoikos, Foteini Oikonomou, Christos Poulos +5Reinforcement Learning Electronic Design Automation
Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece · School of Computing Science, University of Glasgow, UK · Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland
I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning
Oct 5, 2026Ziqi Han, Yitang Li, Junhan Sun +8Representation Learning Reinforcement Learning
Tongji University · Tsinghua University · Zhejiang University +3
Fast Last-Iterate Convergence in Zero-Sum Markov Games with Bandit Feedback
Oct 5, 2026Yuheng ZhangLast-Iterate Convergence Reinforcement Learning
University of Illinois Urbana-Champaign
HuatuoGPT-3: RL-Only Domain Adaptation from Base Models
Oct 5, 2026Junying Chen, Xinyuan Xie, Ziniu Li +7RL for Language Models Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data · Shenzhen Loop Area Institute +1
An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection
Oct 4, 2026Xinyuan Wang, Deepti Agrawal, Yanjie FuReinforcement Learning LLM-Guided RL
School of Computing and AI Arizona State University Tempe, USA
Factoriax: A GPU-Accelerated Factorio-Style Simulator for Reinforcement Learning
Oct 4, 2026Mickey Beurskens, Tristan Tomilin, Thiago D. SimãoRL Benchmarks Reinforcement Learning
Eindhoven University of Technology
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Oct 4, 2026Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu +2Reinforcement Learning Hierarchical RL
LinkedIn Corporation · University of Maryland
Task Inference Beyond Least Squares in Behavioral Foundation Models
Oct 4, 2026Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2Reinforcement Learning Multi-Task RL
National Taiwan University
On Semi-Markov Suboptimality in Hierarchical Reinforcement Learning
Oct 4, 2026Bingyun Liu, Yuheng JingReinforcement Learning Sequential Decision Making
Institute of Automation Chinese Academy of Sciences
Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents
Oct 4, 2026On Tai TangReinforcement Learning Deep Research Agents
The Hong Kong University of Science and Technology (HKUST)
Beyond Instruction Following: Learning Grounded Skill-Following with Skill Contracts
Oct 4, 2026Jianghan Shen, Zhenjie Liu, Yue Li +10Reinforcement Learning LLM Agent Skill Learning
Nanjing University · Shanghai Artificial Intelligence Laboratory · Peking University +3
Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning
Oct 4, 2026S K Swaminathan, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan +1Reinforcement Learning Contrastive RL
Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, India · Department of Mechanical Engineering Indian Institute of Technology Kharagpur, India
InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation
Oct 1, 2026Zhuo Lin, Sirui Xu, Liuyu Bian +2Humanoid Loco-Manipulation Reinforcement Learning
University of Illinois Urbana-Champaign
Faynt: Scaling and Optimizing Policies for Competitive Melee
Oct 1, 2026Ali Janati, Nikita Kuzmin, Rohit Swamy +1Reinforcement Learning Self-Play RL
Frisson Labs
Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos
Oct 1, 2026Tristan Shah, Wooyoung Chung, Volodomyr Makarenko +2Reinforcement Learning Intrinsic Motivation
Texas Tech University · Purdue University

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

Temporally Interpretable Differentiable Decision Trees

Eisuke Hirota, Aarav Sane, Rohan Paleja

Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California

Interpretable ML Interpretability in RL Reinforcement Learning Policy Gradient Methods

Open paper AskPDF arXiv

Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decision making tasks, differentiable decision trees (DDTs) are one approach to such interpretability, maintaining automatic-differentiable policies while providing humans with a discrete tree-based visualization. Nonetheless, current implementations of DDTs are not well-suited for sequential-decision making domains, as there exists an inherent mismatch between a tree's single-timestep behavior and a human's multi-timestep planning. Our work thus introduces time as a new dimension of interpretability, coined as temporal interpretability, and demonstrates how temporal abstractions via action chunking improve it. We achieve this by first introducing two novel policy gradient algorithms that incorporate action chunking. Additionally, to maintain parameter-efficient trees, we develop an information-theoretic tree restructuring algorithm that modifies the tree during training. Across four simulation environments, we find that warm-starting action chunked DDTs from a distilled action chunked policy is the most effective way to obtain temporally interpretable trees: they match neural network policies in three of the four domains while using up to 80

\%

fewer parameters. Our code is available at https://github.com/ei5uke/temp-interp.

Momentum

Related topics

Latest papers 1,138