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 11 / 34 页

501. NeurIPS 2026Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
502. NeurIPS 2026GDMD: Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning
503. NeurIPS 2026Graph Topology Augmentation for Prioritized Sweeping in Non-stationary Reinforcement Learning
504. NeurIPS 2026HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
505. NeurIPS 2026High-Dimensional Robotic Reinforcement Learning with Developing Synergies
506. NeurIPS 2026IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards
507. NeurIPS 2026Imitation Dominates Reinforcement: Direct In-Context RL Is Closer to ICL Than RL
508. NeurIPS 2026Imperfect World Models are Exploitable
509. NeurIPS 2026Instability of Meta-Learning Intrinsic Rewards for Policy Gradient Reinforcement Learning
510. NeurIPS 2026Kernel Value Regression in Offline Reinforcement Learning
511. NeurIPS 2026Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
512. NeurIPS 2026Learning POMDP World Models from Observations with Language-Model Priors
513. NeurIPS 2026Learning to Cut: Reinforcement Learning for Benders Decomposition
514. NeurIPS 2026Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
515. NeurIPS 2026Learning to Surpass: Training Tool-Using Agents with Anchored Feedback
516. NeurIPS 2026Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
517. NeurIPS 2026Learning to Undo: Transfer Reinforcement Learning under State Space Transformations
518. NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
519. NeurIPS 2026Learning When to Think: Adaptive Internal Computation for Reinforcement Learning
520. NeurIPS 2026Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
521. NeurIPS 2026Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery
522. NeurIPS 2026LoopRPT: Reinforcement Pre-Training for Looped Language Models
523. NeurIPS 2026MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning
524. NeurIPS 2026NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
525. NeurIPS 2026Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
526. NeurIPS 2026Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
527. NeurIPS 2026Offline Constrained Reinforcement Learning under Partial Data Coverage
528. NeurIPS 2026On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage
529. NeurIPS 2026One for All: A Non-Linear Transformer can enable Cross-Domain Generalization for In-Context Reinforcement Learning
530. NeurIPS 2026Predictive but Not Plannable: RC-aux for Latent World Models
531. NeurIPS 2026Provably Safe, Yet Performant Reinforcement Learning
532. NeurIPS 2026Quantile Geometry Regularization for Distributional Reinforcement Learning
533. NeurIPS 2026Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
534. NeurIPS 2026Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
535. NeurIPS 2026Reinforcement Learning Agents Are Swimmers
536. NeurIPS 2026Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
537. NeurIPS 2026Reward Hacking in Rubric-Based Reinforcement Learning
538. NeurIPS 2026Reward Inflation: A Healthy Stimulus for Reinforcement Learning
539. NeurIPS 2026Reward-Conditioned Reinforcement Learning
540. NeurIPS 2026Reward-Estimated Hypergradient for Bilevel Reinforcement Learning with Black-Box Follower
541. NeurIPS 2026Robust Offline Reinforcement Learning against Out-of-Distribution Dynamics in Autonomous Driving
542. NeurIPS 2026SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
543. NeurIPS 2026Sampling-Based Safe Reinforcement Learning
544. NeurIPS 2026SEAR: Sample Efficient Action Chunking Reinforcement Learning
545. NeurIPS 2026SIRAS: Sibling-Relative Advantage Shaping for Reinforcement Learning from Verifiable Rewards
546. NeurIPS 2026SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
547. NeurIPS 2026Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
548. NeurIPS 2026Spectral Representations for Provably Robust Offline Meta-Reinforcement Learning from Bagged Rewards
549. NeurIPS 2026Split-RL: Local Conflict Resolution in Reinforcement Learning
550. NeurIPS 2026STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

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。