General ML · reinforcement learning papers, 2023–2026

Key findings

• General ML · reinforcement learning went from 287 papers in 2023 to 575 in 2026, roughly 2.0x in three years.
• ICLR publishes the most General ML · reinforcement learning work: 847 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 34 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 2.0x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All General ML · reinforcement learning papers

411 papers, page 7 of 9

301. ICML 2025Universal Approximation Theorem of Deep Q-Networks
302. ICML 2025Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
303. ICML 2025Vintix: Action Model via In-Context Reinforcement Learning
304. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
305. NeurIPS 2025A Clean Slate for Offline Reinforcement Learning
306. NeurIPS 2025A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits
307. NeurIPS 2025A Provable Approach for End-to-End Safe Reinforcement Learning
308. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
309. NeurIPS 2025Actor-Free Continuous Control via Structurally Maximizable Q-Functions
310. NeurIPS 2025Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
311. NeurIPS 2025Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
312. NeurIPS 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
313. NeurIPS 2025Approximating Shapley Explanations in Reinforcement Learning
314. NeurIPS 2025Automaton Constrained Q-Learning
315. NeurIPS 2025Behavior Injection: Preparing Language Models for Reinforcement Learning
316. NeurIPS 2025Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
317. NeurIPS 2025Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
318. NeurIPS 2025Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
319. NeurIPS 2025BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
320. NeurIPS 2025Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
321. NeurIPS 2025Capturing Individual Human Preferences with Reward Features
322. NeurIPS 2025Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
323. NeurIPS 2025COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
324. NeurIPS 2025Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
325. NeurIPS 2025Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
326. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
327. NeurIPS 2025DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
328. NeurIPS 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale
329. NeurIPS 2025DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
330. NeurIPS 2025Doubly Robust Alignment for Large Language Models
331. NeurIPS 2025Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph
332. NeurIPS 2025Efficient Preference-Based Reinforcement Learning: Randomized Exploration meets Experimental Design
333. NeurIPS 2025Eluder dimension: localise it!
334. NeurIPS 2025Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
335. NeurIPS 2025Explainably Safe Reinforcement Learning
336. NeurIPS 2025FANS: A Flatness-Aware Network Structure for Generalization in Offline Reinforcement Learning
337. NeurIPS 2025Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
338. NeurIPS 2025From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
339. NeurIPS 2025Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
340. NeurIPS 2025General-Reasoner: Advancing LLM Reasoning Across All Domains
341. NeurIPS 2025Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
342. NeurIPS 2025Greedy Sampling Is Provably Efficient For RLHF
343. NeurIPS 2025Gymnasium: A Standard Interface for Reinforcement Learning Environments
344. NeurIPS 2025Horizon Reduction Makes RL Scalable
345. NeurIPS 2025How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
346. NeurIPS 2025Imagined Autocurricula
347. NeurIPS 2025Imitation Beyond Expectation Using Pluralistic Stochastic Dominance
348. NeurIPS 2025Imitation Learning with Temporal Logic Constraints
349. NeurIPS 2025Improving Model-Based Reinforcement Learning by Converging to Flatter Minima
350. NeurIPS 2025Improving Reward Models with Proximal Policy Exploration for Preference-Based Reinforcement Learning

FAQ

How many General ML · reinforcement learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,676 in total: 287 in 2023, 403 in 2024, 411 in 2025, 575 in 2026.

How many General ML · reinforcement learning papers are at NeurIPS 2026?

NeurIPS 2026 has 113 General ML · reinforcement learning papers, versus 108 the year before.

How many General ML · reinforcement learning papers are at ICLR 2026?

ICLR 2026 has 335 General ML · reinforcement learning papers, versus 230 the year before.

How many General ML · reinforcement learning papers are at ICML 2026?

ICML 2026 has 113 General ML · reinforcement learning papers, versus 71 the year before.

How many General ML · reinforcement learning papers are at CVPR 2026?

CVPR 2026 has 14 General ML · reinforcement learning papers, versus 2 the year before.

Which conference publishes the most General ML · reinforcement learning papers?

ICLR, with 847 papers from 2023 to 2026.