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 1,676 篇，第 32 / 34 页

1551. NeurIPS 2023Conservative State Value Estimation for Offline Reinforcement Learning
1552. NeurIPS 2023Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning
1553. NeurIPS 2023Context Shift Reduction for Offline Meta-Reinforcement Learning
1554. NeurIPS 2023Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
1555. NeurIPS 2023Corruption-Robust Offline Reinforcement Learning with General Function Approximation
1556. NeurIPS 2023Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
1557. NeurIPS 2023CQM: Curriculum Reinforcement Learning with a Quantized World Model
1558. NeurIPS 2023Creating Multi-Level Skill Hierarchies in Reinforcement Learning
1559. NeurIPS 2023Cross-Episodic Curriculum for Transformer Agents
1560. NeurIPS 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
1561. NeurIPS 2023Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
1562. NeurIPS 2023Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
1563. NeurIPS 2023Deep Reinforcement Learning with Plasticity Injection
1564. NeurIPS 2023Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
1565. NeurIPS 2023Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
1566. NeurIPS 2023Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
1567. NeurIPS 2023Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
1568. NeurIPS 2023Doubly Robust Augmented Transfer for Meta-Reinforcement Learning
1569. NeurIPS 2023Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning
1570. NeurIPS 2023Efficient Exploration in Continuous-time Model-based Reinforcement Learning
1571. NeurIPS 2023Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
1572. NeurIPS 2023Elastic Decision Transformer
1573. NeurIPS 2023ELDEN: Exploration via Local Dependencies
1574. NeurIPS 2023f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences
1575. NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
1576. NeurIPS 2023Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
1577. NeurIPS 2023Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning
1578. NeurIPS 2023Goal-Conditioned Predictive Coding for Offline Reinforcement Learning
1579. NeurIPS 2023HIQL: Offline Goal-Conditioned RL with Latent States as Actions
1580. NeurIPS 2023Hybrid Policy Optimization from Imperfect Demonstrations
1581. NeurIPS 2023Hybrid Search for Efficient Planning with Completeness Guarantees
1582. NeurIPS 2023Hyperbolic VAE via Latent Gaussian Distributions
1583. NeurIPS 2023Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
1584. NeurIPS 2023Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
1585. NeurIPS 2023Information Design in Multi-Agent Reinforcement Learning
1586. NeurIPS 2023Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives
1587. NeurIPS 2023Inverse Preference Learning: Preference-based RL without a Reward Function
1588. NeurIPS 2023Inverse Reinforcement Learning with the Average Reward Criterion
1589. NeurIPS 2023Is RLHF More Difficult than Standard RL? A Theoretical Perspective
1590. NeurIPS 2023Iterative Reachability Estimation for Safe Reinforcement Learning
1591. NeurIPS 2023Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
1592. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
1593. NeurIPS 2023KuaiSim: A Comprehensive Simulator for Recommender Systems
1594. NeurIPS 2023Latent exploration for Reinforcement Learning
1595. NeurIPS 2023Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
1596. NeurIPS 2023Learning Generalizable Agents via Saliency-guided Features Decorrelation
1597. NeurIPS 2023Learning Regularized Monotone Graphon Mean-Field Games
1598. NeurIPS 2023Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
1599. NeurIPS 2023Learning to Influence Human Behavior with Offline Reinforcement Learning
1600. NeurIPS 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

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。