强化学习论文趋势 2023–2026

主要结论

• 强化学习论文从 2023 年的 1,070 篇增长到 2026 年的 2,985 篇，三年间扩大到约 2.8 倍。
• NeurIPS 是发强化学习论文最多的会议，2023–2026 年共 2,494 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是推理模型：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 437 篇。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
• ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
• ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
• CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
• NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
• ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
• ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
• CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
• NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
• ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
• ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
• CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
• NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
• ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
• ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
• CVPR 2023"Seeing" Electric Network Frequency From Events
• NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
• ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
• ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
• CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition

全部强化学习论文

共 6,885 篇，第 90 / 138 页

4451. NeurIPS 2025SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents
4452. NeurIPS 2025Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
4453. NeurIPS 2025Seeing the Arrow of Time in Large Multimodal Models
4454. NeurIPS 2025Seeing through Uncertainty: Robust Task-Oriented Optimization in Visual Navigation
4455. NeurIPS 2025Seg-VAR:Image Segmentation with Visual Autoregressive Modeling
4456. NeurIPS 2025Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models
4457. NeurIPS 2025Self-Adapting Language Models
4458. NeurIPS 2025Self-Challenging Language Model Agents
4459. NeurIPS 2025Self-Guided Hierarchical Exploration for Generalist Foundation Model Web Agents
4460. NeurIPS 2025Self-Improving Embodied Foundation Models
4461. NeurIPS 2025Self-Verifying Reflection Helps Transformers with CoT Reasoning
4462. NeurIPS 2025Selftok-Zero: Reinforcement Learning for Visual Generation via Discrete and Autoregressive Visual Tokens
4463. NeurIPS 2025Semantic-guided Diverse Decoding for Large Language Model
4464. NeurIPS 2025Semi-off-Policy Reinforcement Learning for Vision-Language Slow-Thinking Reasoning
4465. NeurIPS 2025Sequential Attention-based Sampling for Histopathological Analysis
4466. NeurIPS 2025Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
4467. NeurIPS 2025Sequential Multi-Agent Dynamic Algorithm Configuration
4468. NeurIPS 2025SeRL: Self-play Reinforcement Learning for Large Language Models with Limited Data
4469. NeurIPS 2025Sharp Analysis for KL-Regularized Contextual Bandits and RLHF
4470. NeurIPS 2025Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs
4471. NeurIPS 2025Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
4472. NeurIPS 2025ShiQ: Bringing back Bellman to LLMs
4473. NeurIPS 2025ShorterBetter: Guiding Reasoning Models to Find Optimal Inference Length for Efficient Reasoning
4474. NeurIPS 2025Simultaneous Statistical Inference for Off-Policy Evaluation in Reinforcement Learning
4475. NeurIPS 2025Skill-Driven Neurosymbolic State Abstractions
4476. NeurIPS 2025Social World Model-Augmented Mechanism Design Policy Learning
4477. NeurIPS 2025Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
4478. NeurIPS 2025Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
4479. NeurIPS 2025SOMBRL: Scalable and Optimistic Model-Based RL
4480. NeurIPS 2025SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound
4481. NeurIPS 2025SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration
4482. NeurIPS 2025Spatial-Aware Decision-Making with Ring Attractors in Reinforcement Learning Systems
4483. NeurIPS 2025SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning
4484. NeurIPS 2025Spectral Learning for Infinite-Horizon Average-Reward POMDPs
4485. NeurIPS 2025SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
4486. NeurIPS 2025SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
4487. NeurIPS 2025SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning
4488. NeurIPS 2025SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
4489. NeurIPS 2025Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
4490. NeurIPS 2025Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning
4491. NeurIPS 2025STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning
4492. NeurIPS 2025STAR: Efficient Preference-based Reinforcement Learning via Dual Regularization
4493. NeurIPS 2025State Entropy Regularization for Robust Reinforcement Learning
4494. NeurIPS 2025State-Covering Trajectory Stitching for Diffusion Planners
4495. NeurIPS 2025Steering Generative Models with Experimental Data for Protein Fitness Optimization
4496. NeurIPS 2025STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
4497. NeurIPS 2025Stochastic Principal-Agent Problems: Computing and Learning Optimal History-Dependent Policies
4498. NeurIPS 2025Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning
4499. NeurIPS 2025Strategyproof Reinforcement Learning from Human Feedback
4500. NeurIPS 2025Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning

常见问题

2023–2026 年四大会议一共有多少篇强化学习论文？

共 6,885 篇，按年份分别是 2023 年 1,070 篇、2024 年 1,190 篇、2025 年 1,640 篇、2026 年 2,985 篇。

NeurIPS 2026 有多少篇强化学习论文？

NeurIPS 2026 有 760 篇强化学习论文，上一届是 784 篇。

ICLR 2026 有多少篇强化学习论文？

ICLR 2026 有 911 篇强化学习论文，上一届是 379 篇。

ICML 2026 有多少篇强化学习论文？

ICML 2026 有 1,021 篇强化学习论文，上一届是 405 篇。

CVPR 2026 有多少篇强化学习论文？

CVPR 2026 有 293 篇强化学习论文，上一届是 72 篇。

哪个会议的强化学习论文最多？

NeurIPS，2023–2026 年共 2,494 篇。