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 9 / 34 页

401. ICML 2026Learning What to Generate: A Reinforcement Learning-based Closed-Loop Augmentation Framework for Person Re-identification
402. ICML 2026Learning-To-Measure: In-Context Active Feature Acquisition
403. ICML 2026LightningRL: Breaking the Accuracy–Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning
404. ICML 2026Mind the Budget: Accelerating Deep Reinforcement Learning using Constrained Early Exit Neural Networks
405. ICML 2026Mirror Descent Actor Critic via Bounded Advantage Learning
406. ICML 2026Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
407. ICML 2026Multivariate Distributional Reinforcement Learning Using Sliced Divergences
408. ICML 2026Neuro-evolutionary Continual Reinforcement Learning
409. ICML 2026Noise-Guided Transport: Imitation Learning from Random Priors
410. ICML 2026Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
411. ICML 2026Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment
412. ICML 2026Offline Reinforcement Learning with Universal Horizon Models
413. ICML 2026On the Role of Computation in Reinforcement Learning
414. ICML 2026Online Robust Reinforcement Learning with General Function Approximation
415. ICML 2026PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
416. ICML 2026PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning
417. ICML 2026PAWS: Preference Learning with Advantage-Weighted Segments
418. ICML 2026Performative Policy Gradient: Optimality in Performative Reinforcement Learning
419. ICML 2026PretrainZero: Reinforcement Active Learning on Pretraining Data
420. ICML 2026Prioritized Model Experience Replay
421. ICML 2026PRM-PBE: Process Reward Model for Reinforcement Learning in Programming-by-Example
422. ICML 2026Q-SAM: Unlocking Sharpness-Aware Minimization for Generalization in Offline Reinforcement Learning
423. ICML 2026Quantum Robust Inner Minimization for Reinforcement Learning with Quadratic Speed-Up in Query Complexity
424. ICML 2026Rate or Fate? RLV$^{\varepsilon}$R: Reinforcement Learning with Verifiable Noisy Rewards
425. ICML 2026Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality
426. ICML 2026Reinforcement Learning via Self-Distillation
427. ICML 2026Reinforcement Learning with Action-Triggered Observations
428. ICML 2026Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
429. ICML 2026Rethinking the Hardness of PbRL: A Provable General Regret Bound
430. ICML 2026Rethinking the Trust Region in LLM Reinforcement Learning
431. ICML 2026Return-Critic: Bridging Goal Discrepancy for Efficient Visual Reinforcement Learning
432. ICML 2026Reward-Preserving Counterfactual State Editing for Offline Reinforcement Learning
433. ICML 2026RiskZero: Plan More to Risk Less with a Learned Model
434. ICML 2026RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
435. ICML 2026Robust Sequential Experimental Design for A/B Testing
436. ICML 2026Safe In-Context Reinforcement Learning
437. ICML 2026Safe Reinforcement Learning with Preference-based Constraint Inference
438. ICML 2026Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed
439. ICML 2026Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
440. ICML 2026Scalable Option Learning in High-Throughput Environments
441. ICML 2026SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
442. ICML 2026SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
443. ICML 2026Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
444. ICML 2026StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
445. ICML 2026SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
446. ICML 2026SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
447. ICML 2026Taming Aleatoric Impulse in Off-Policy Reinforcement Learning
448. ICML 2026Test-time Offline Reinforcement Learning on Goal-related Experience
449. ICML 2026The Cost of Commitment in Option-Based Hierarchical RL
450. ICML 2026The Role of Target Update Frequencies in Q-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。