通用机器学习 · reinforcement learning论文趋势 2023–2026

主要结论

• 通用机器学习 · reinforcement learning论文从 2023 年的 287 篇增长到 2026 年的 575 篇，三年间扩大到约 2.0 倍。
• ICLR 是发通用机器学习 · reinforcement learning论文最多的会议，2023–2026 年共 847 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 2 篇增至 2026 年的 34 篇。
• 在「通用机器学习」的 6 个主题里，它的 2026 年论文数排第 2；同组增长最快的通用机器学习 · evaluation是 4.3 倍，通用机器学习 · reinforcement learning是 2.0 倍。

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 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
• ICML 2026De-attribute to Forget for LLM Unlearning
• ICLR 2026AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
• CVPR 2026Dynamic Important Example Mining for Reinforcement Finetuning
• NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
• ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
• ICLR 2025A General Framework for Off-Policy Learning with Partially-Observed Reward
• CVPR 2025Automated Proof of Polynomial Inequalities via Reinforcement Learning
• NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
• ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
• ICLR 2024Bandits with Replenishable Knapsacks: the Best of both Worlds
• CVPR 2024Improving Unsupervised Hierarchical Representation with Reinforcement Learning
• NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
• ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
• ICLR 2023A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
• CVPR 2023Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
• NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
• ICML 2026Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
• ICLR 2026APPLE: Toward General Active Perception via Reinforcement Learning
• CVPR 2026Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

全部通用机器学习 · reinforcement learning论文

共 468 篇，第 9 / 10 页

401. NeurIPS 2023Monte Carlo Tree Search with Boltzmann Exploration
402. NeurIPS 2023Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
403. NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
404. NeurIPS 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
405. NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
406. NeurIPS 2023OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
407. NeurIPS 2023Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
408. NeurIPS 2023Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
409. NeurIPS 2023Offline Reinforcement Learning with Differential Privacy
410. NeurIPS 2023On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
411. NeurIPS 2023On the Importance of Exploration for Generalization in Reinforcement Learning
412. NeurIPS 2023One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
413. NeurIPS 2023Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
414. NeurIPS 2023Optimistic Active Exploration of Dynamical Systems
415. NeurIPS 2023Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
416. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
417. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
418. NeurIPS 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
419. NeurIPS 2023PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
420. NeurIPS 2023Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
421. NeurIPS 2023Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
422. NeurIPS 2023Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
423. NeurIPS 2023Prioritizing Samples in Reinforcement Learning with Reducible Loss
424. NeurIPS 2023Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
425. NeurIPS 2023Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
426. NeurIPS 2023Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
427. NeurIPS 2023Provably Safe Reinforcement Learning with Step-wise Violation Constraints
428. NeurIPS 2023Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
429. NeurIPS 2023Reduced Policy Optimization for Continuous Control with Hard Constraints
430. NeurIPS 2023Reflexion: language agents with verbal reinforcement learning
431. NeurIPS 2023Regret Minimization via Saddle Point Optimization
432. NeurIPS 2023Reinforcement Learning with Simple Sequence Priors
433. NeurIPS 2023Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
434. NeurIPS 2023Reining Generalization in Offline Reinforcement Learning via Representation Distinction
435. NeurIPS 2023Replicability in Reinforcement Learning
436. NeurIPS 2023Replicable Reinforcement Learning
437. NeurIPS 2023Revisiting the Minimalist Approach to Offline Reinforcement Learning
438. NeurIPS 2023Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
439. NeurIPS 2023Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
440. NeurIPS 2023Rewiring Neurons in Non-Stationary Environments
441. NeurIPS 2023RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
442. NeurIPS 2023Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark
443. NeurIPS 2023Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
444. NeurIPS 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
445. NeurIPS 2023Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
446. NeurIPS 2023Self-Predictive Universal AI
447. NeurIPS 2023Sequential Preference Ranking for Efficient Reinforcement Learning from Human Feedback
448. NeurIPS 2023SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
449. NeurIPS 2023Small batch deep reinforcement learning
450. NeurIPS 2023SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning

常见问题

2023–2026 年四大会议一共有多少篇通用机器学习 · reinforcement learning论文？

共 1,676 篇，按年份分别是 2023 年 287 篇、2024 年 403 篇、2025 年 411 篇、2026 年 575 篇。

NeurIPS 2026 有多少篇通用机器学习 · reinforcement learning论文？

NeurIPS 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 108 篇。

ICLR 2026 有多少篇通用机器学习 · reinforcement learning论文？

ICLR 2026 有 335 篇通用机器学习 · reinforcement learning论文，上一届是 230 篇。

ICML 2026 有多少篇通用机器学习 · reinforcement learning论文？

ICML 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 71 篇。

CVPR 2026 有多少篇通用机器学习 · reinforcement learning论文？

CVPR 2026 有 14 篇通用机器学习 · reinforcement learning论文，上一届是 2 篇。

哪个会议的通用机器学习 · reinforcement learning论文最多？

ICLR，2023–2026 年共 847 篇。