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 2,494 篇，第 9 / 50 页

401. NeurIPS 2026MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation
402. NeurIPS 2026Mitigating Compounding Errors in Online Reinforcement Learning via Optimal Transport Regularized Flow Matching
403. NeurIPS 2026Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
404. NeurIPS 2026Mitigating Overgeneralization in RND via Spectral Target Design
405. NeurIPS 2026Mitigating Retaliatory Algorithmic Collusion in Repeated Games
406. NeurIPS 2026Mitigating Reward Hacking via Task Representations
407. NeurIPS 2026Model-based Bootstrap of Controlled Markov Chains
408. NeurIPS 2026Modeling quantum neural network gradient with reinforcement learning
409. NeurIPS 2026Modelling Opinion Dynamics at Scale with Deep MARL
410. NeurIPS 2026MOSAIC: Scaling Long-Horizon Language Agents via Multi-Scale Adaptive Inference Control
411. NeurIPS 2026MPQ: A Message-Passing View of Post-Training Quantization
412. NeurIPS 2026Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
413. NeurIPS 2026Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
414. NeurIPS 2026Multi-Stage Planning from Single-Stage Data: Reinforcement Learning Helps Composition but Requires Anchoring
415. NeurIPS 2026Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
416. NeurIPS 2026Multi-Turn RL Makes Small Language Model Competitive for Optimization Modeling
417. NeurIPS 2026NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
418. NeurIPS 2026Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit Feedback
419. NeurIPS 2026Need-Aware Multi-Objective Reinforcement Learning for Emotionally Intelligent LLM Agents
420. NeurIPS 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
421. NeurIPS 2026Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
422. NeurIPS 2026NestRL: A Nested Training Regime for Mutual Adaptation in Human–AI Teaming
423. NeurIPS 2026Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors
424. NeurIPS 2026Neural Garbage Collection: Learning to Forget while Learning to Reason
425. NeurIPS 2026Neural Modal Decomposition: Architectural Priors from Observables
426. NeurIPS 2026Neural Spectral Capacity: An Architectural Quantity from Network Specification Alone
427. NeurIPS 2026Neuro-Inspired Inverse Learning for Planning and Control
428. NeurIPS 2026Never Stop Learning: Test-Time Reinforcement Learning for Vision-Language-Action Models
429. NeurIPS 2026NitroBox: Lightning-Fast Sandbox for Large-Scale RL Training
430. NeurIPS 2026Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
431. NeurIPS 2026Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
432. NeurIPS 2026Not All Tokens Should Be Treated Equally: Context Credits Reassignment
433. NeurIPS 2026Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
434. NeurIPS 2026NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models
435. NeurIPS 2026Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders
436. NeurIPS 2026Objective-Aligned Amortized Inference for Offline Bayes-Adaptive MDP Model Learning
437. NeurIPS 2026Off-policy Learning with Excursion Policies
438. NeurIPS 2026Offline Constrained Reinforcement Learning under Partial Data Coverage
439. NeurIPS 2026Offline Inverse Reinforcement Learning with Unified Diffusion Planning
440. NeurIPS 2026Offline Materials Optimization with CliqueFlowmer
441. NeurIPS 2026Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
442. NeurIPS 2026OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models
443. NeurIPS 2026On the Complexity of Discounted Robust MDPs with $L_p$ Uncertainty Sets
444. NeurIPS 2026On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage
445. NeurIPS 2026On the Convergence of Success Conditioning for Policy Optimization
446. NeurIPS 2026On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
447. NeurIPS 2026On the Origin of Algorithmic Progress in AI: Evidence from Language Model Pre-Training
448. NeurIPS 2026On-Policy Counterfactual Influence
449. NeurIPS 2026One for All: A Non-Linear Transformer can enable Cross-Domain Generalization for In-Context Reinforcement Learning
450. NeurIPS 2026One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

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。