Reinforcement Learning papers, 2023–2026

Key findings

• Reinforcement Learning went from 1,070 papers in 2023 to 2,985 in 2026, roughly 2.8x in three years.
• NeurIPS publishes the most Reinforcement Learning work: 2,494 papers from 2023 to 2026, 36% of the four-venue total.
• The fastest-growing overlap is with Reasoning Models: papers tagged with both rose from 9 in 2023 to 437 in 2026.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
• ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
• ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
• CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
• NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
• ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
• ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
• CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
• NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
• ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
• ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
• CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
• NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
• ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
• ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
• CVPR 2023"Seeing" Electric Network Frequency From Events
• NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
• ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
• ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
• CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition

All Reinforcement Learning papers

2,985 papers, page 58 of 60

2851. NeurIPS 2026STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
2852. NeurIPS 2026StateTree: Enhancing Long-term Dialogue Reasoning via Reinforcement Learning
2853. NeurIPS 2026Statistical Complexity of Soft Bellman Residual Minimization
2854. NeurIPS 2026Steer2Edit: From Activation Steering to Component-Level Editing
2855. NeurIPS 2026Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces
2856. NeurIPS 2026Step-wise Rubric Rewards for LLM Reasoning
2857. NeurIPS 2026Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning
2858. NeurIPS 2026StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
2859. NeurIPS 2026Strategic Decision Focused Learning
2860. NeurIPS 2026Strengthening LLMs for Tabular Prediction with Structural Priors
2861. NeurIPS 2026STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning
2862. NeurIPS 2026Structural Entropy Optimized Communication for Multi-Agent Reinforcement Learning
2863. NeurIPS 2026Structure-aware Reinforcement Learning for Protein Directed Evolution
2864. NeurIPS 2026Subprocess-Constrained Markov Decision Processes
2865. NeurIPS 2026Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
2866. NeurIPS 2026SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents
2867. NeurIPS 2026Synsema: Syntax-Guided Learning of Semantically Valid Programs
2868. NeurIPS 2026Tail-Risk-Aware Stackelberg Learning
2869. NeurIPS 2026Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement
2870. NeurIPS 2026Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
2871. NeurIPS 2026TANGO: RNA Topology and Geometry Co-Design
2872. NeurIPS 2026TARP: Trace-Anchored Regularization Prior for Retaining Stepwise Reasoning
2873. NeurIPS 2026Teach-to-Reason: Competition-Guided Reasoning with a Self-Improving Teacher
2874. NeurIPS 2026TEMPO: Temporal Enforcement via Mode-Separated Policy Optimization for Trustworthy LLM Backtesting
2875. NeurIPS 2026Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
2876. NeurIPS 2026Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning
2877. NeurIPS 2026Temporal Selective Exploration for Reinforcement Learning-Guided Continuous-Discrete Flow Matching in 3D Molecular Design
2878. NeurIPS 2026Test-time Multi-agent Coordination by Decomposed Value Gradient Flow
2879. NeurIPS 2026Test-time Risk Adaptation with Mixture of Agents
2880. NeurIPS 2026TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks
2881. NeurIPS 2026TGPO: Trace-Guided Policy Optimization for Robot Task Planning via Verifiable Subgoal Generation
2882. NeurIPS 2026TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs
2883. NeurIPS 2026The Benefits of Temporal Correlations: SGD Efficiently Learns k-Juntas from Random Walks
2884. NeurIPS 2026The Capability Frontier of GRPO
2885. NeurIPS 2026The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning
2886. NeurIPS 2026The Era of Agentic Organization: Learning to Organize with Language Models
2887. NeurIPS 2026The Horizon Threshold in Cooperative Multi-Agent Reward-Free Exploration
2888. NeurIPS 2026The Illusion of Diversity: Aligning LLM Exploration via Effective Entropy
2889. NeurIPS 2026The Laplacian Keyboard: Beyond the Linear Span
2890. NeurIPS 2026The Many Faces of On-Policy Distillation: Pitfalls, Mechanisms, and Fixes
2891. NeurIPS 2026The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
2892. NeurIPS 2026The Post-Training Dilemma: Why We Should Rethink the Sequential SFT-RL Paradigm
2893. NeurIPS 2026The Reasoning Boundary Paradox: How Reinforcement Learning Constrains Language Models
2894. NeurIPS 2026The Reciprocity Gradient
2895. NeurIPS 2026The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL.
2896. NeurIPS 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
2897. NeurIPS 2026The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
2898. NeurIPS 2026The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
2899. NeurIPS 2026The Type Theory of Stationary MDPs: Rare Events and Uncertainty Quantification
2900. NeurIPS 2026Theoretical Limits of Language Model Alignment

FAQ

How many Reinforcement Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

6,885 in total: 1,070 in 2023, 1,190 in 2024, 1,640 in 2025, 2,985 in 2026.

How many Reinforcement Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 760 Reinforcement Learning papers, versus 784 the year before.

How many Reinforcement Learning papers are at ICLR 2026?

ICLR 2026 has 911 Reinforcement Learning papers, versus 379 the year before.

How many Reinforcement Learning papers are at ICML 2026?

ICML 2026 has 1,021 Reinforcement Learning papers, versus 405 the year before.

How many Reinforcement Learning papers are at CVPR 2026?

CVPR 2026 has 293 Reinforcement Learning papers, versus 72 the year before.

Which conference publishes the most Reinforcement Learning papers?

NeurIPS, with 2,494 papers from 2023 to 2026.