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 466 篇，第 7 / 10 页

301. NeurIPS 2024OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations
302. NeurIPS 2024Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
303. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
304. NeurIPS 2024Optimizing Automatic Differentiation with Deep Reinforcement Learning
305. NeurIPS 2024Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition
306. NeurIPS 2024Oracle-Efficient Reinforcement Learning for Max Value Ensembles
307. NeurIPS 2024Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
308. NeurIPS 2024Parallelizing Model-based Reinforcement Learning Over the Sequence Length
309. NeurIPS 2024Parseval Regularization for Continual Reinforcement Learning
310. NeurIPS 2024Paths to Equilibrium in Games
311. NeurIPS 2024PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
312. NeurIPS 2024Periodic agent-state based Q-learning for POMDPs
313. NeurIPS 2024Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
314. NeurIPS 2024Pessimistic Backward Policy for GFlowNets
315. NeurIPS 2024Policy Aggregation
316. NeurIPS 2024Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting
317. NeurIPS 2024Policy Mirror Descent with Lookahead
318. NeurIPS 2024Policy Optimization for Robust Average Reward MDPs
319. NeurIPS 2024Policy-shaped prediction: avoiding distractions in model-based reinforcement learning
320. NeurIPS 2024Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
321. NeurIPS 2024Predicting Future Actions of Reinforcement Learning Agents
322. NeurIPS 2024Preference Alignment with Flow Matching
323. NeurIPS 2024PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
324. NeurIPS 2024Provable Partially Observable Reinforcement Learning with Privileged Information
325. NeurIPS 2024Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
326. NeurIPS 2024Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
327. NeurIPS 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
328. NeurIPS 2024PUZZLES: A Benchmark for Neural Algorithmic Reasoning
329. NeurIPS 2024Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
330. NeurIPS 2024QGFN: Controllable Greediness with Action Values
331. NeurIPS 2024QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
332. NeurIPS 2024RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning
333. NeurIPS 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
334. NeurIPS 2024Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
335. NeurIPS 2024Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
336. NeurIPS 2024Real-Time Recurrent Learning using Trace Units in Reinforcement Learning
337. NeurIPS 2024REBEL: Reinforcement Learning via Regressing Relative Rewards
338. NeurIPS 2024REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
339. NeurIPS 2024Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents
340. NeurIPS 2024Recurrent Reinforcement Learning with Memoroids
341. NeurIPS 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improve
342. NeurIPS 2024Regularized Conditional Diffusion Model for Multi-Task Preference Alignment
343. NeurIPS 2024Regularized Q-Learning
344. NeurIPS 2024Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
345. NeurIPS 2024Reinforced Cross-Domain Knowledge Distillation on Time Series Data
346. NeurIPS 2024Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers
347. NeurIPS 2024Reinforcement Learning Guided Semi-Supervised Learning
348. NeurIPS 2024Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
349. NeurIPS 2024Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
350. NeurIPS 2024Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems

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。