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 18 / 34 页

851. ICML 2025Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
852. ICML 2025Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
853. ICML 2025Partially Observable Reinforcement Learning with Memory Traces
854. ICML 2025Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning
855. ICML 2025Polynomial-Time Approximability of Constrained Reinforcement Learning
856. ICML 2025Position: Constants are Critical in Regret Bounds for Reinforcement Learning
857. ICML 2025Position: General Intelligence Requires Reward-based Pretraining
858. ICML 2025Position: Lifetime tuning is incompatible with continual reinforcement learning
859. ICML 2025Provable Maximum Entropy Manifold Exploration via Diffusion Models
860. ICML 2025Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
861. ICML 2025QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal
862. ICML 2025Regret-Free Reinforcement Learning for Temporal Logic Specifications
863. ICML 2025Reinforcement Learning with Random Time Horizons
864. ICML 2025Reinforcement Learning with Segment Feedback
865. ICML 2025Reward Translation via Reward Machine in Semi-Alignable MDPs
866. ICML 2025Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
867. ICML 2025Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization
868. ICML 2025RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations
869. ICML 2025Sleeping Reinforcement Learning
870. ICML 2025Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning
871. ICML 2025Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
872. ICML 2025Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
873. ICML 2025The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
874. ICML 2025The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
875. ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
876. ICML 2025Universal Approximation Theorem of Deep Q-Networks
877. ICML 2025Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
878. ICML 2025Vintix: Action Model via In-Context Reinforcement Learning
879. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
880. NeurIPS 2025A Clean Slate for Offline Reinforcement Learning
881. NeurIPS 2025A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits
882. NeurIPS 2025A Provable Approach for End-to-End Safe Reinforcement Learning
883. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
884. NeurIPS 2025Actor-Free Continuous Control via Structurally Maximizable Q-Functions
885. NeurIPS 2025Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
886. NeurIPS 2025Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
887. NeurIPS 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
888. NeurIPS 2025Approximating Shapley Explanations in Reinforcement Learning
889. NeurIPS 2025Automaton Constrained Q-Learning
890. NeurIPS 2025Behavior Injection: Preparing Language Models for Reinforcement Learning
891. NeurIPS 2025Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
892. NeurIPS 2025Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
893. NeurIPS 2025Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
894. NeurIPS 2025BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
895. NeurIPS 2025Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
896. NeurIPS 2025Capturing Individual Human Preferences with Reward Features
897. NeurIPS 2025Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
898. NeurIPS 2025COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
899. NeurIPS 2025Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
900. NeurIPS 2025Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data

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。