RL for Code Generation

RL: Reinforcement Learning

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14 papers in the last four weeks, up 367% on the four weeks before. 0.1% of all new papers.

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BoT-GRPO: Efficient Process-Reward RL for Reasoning via Bag-of-Token Aggregation
Oct 7, 2026Yingxiang Yang, Weihang Xiao, Zhunxuan Wang +2RL for Language Model Reasoning Group Relative Policy Optimization
Amazon AGI · Virginia Tech
AGAR: a reinforcement learning substrate for LLM program evolution
Oct 6, 2026Haoran Li, Zengle Ge, Xiaomin Yuan +11LLM-Based Program Synthesis RL for Code Generation
University of the Chinese Academy of Sciences · Baidu · Tsinghua University +2
FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents
Oct 6, 2026Jia Liufu, Bin Hu, Linglin Jing +5RL for Code Generation Software Engineering Agents
NVIDIA · Tsinghua University
CodeForge-MA: Execution-Verified Multi-Agent Learning with Language-Conditioned LoRA for Multilingual Code Generation
Oct 4, 2026Zhizhou Gu, Xianting Wu, Siyu Gu +2RL for Code Generation Synthetic Data Generation for Language Models
Northeastern University Bellevue, USA · Columbia University New York, USA · University of Pennsylvania Philadelphia, USA +2
CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning
Oct 1, 2026Yafei Zhang, Songshuo Lu, Sicong Liao +2RL for Code Generation RL for Language Models
Moore Threads AI
Cross-Benchmark Transfer from RL on Agentic Coding Tasks
Oct 1, 2026Sushant Mehta, Logan Ritchie, Edwin ChenRL for Code Generation AI Agent Benchmarks
Surge AI
CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL
Sep 30, 2026Shouli Wang, Yanfeng Jia, Zhihao Ou +6RL Benchmarks RL for Code Generation
Tsinghua University · Beihang University · Southern University of Science and Technology +2
RLTL;DR: Self-improvement by Internalizing Self-generated Feedback
Sep 29, 2026Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6RL for Code Generation Reinforcement Learning with Verifiable Rewards
Apple
Reinforcement Learning from Intermediate Renders for Image-to-Code Generation
Sep 28, 2026Omri Kaduri, Kate Feingold, Phillip Isola +1RL for Code Generation Code Generation
Weizmann Institute of Science · MIT
Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
Sep 27, 2026Yuanhao Li, Hongbo Wang, Xuhong Chen +2RL for Code Generation Software Engineering Agents
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing 100876, China · University of Luxembourg, Luxembourg
FLARE: A Full-Lifecycle Dense Supervision Paradigm for Long-Horizon Coding Agents via Generative Reward Model
Sep 20, 2026Jingxuan Xu, Gang Wu, Yanan Wu +13RL for Code Generation Reward Modeling
Independent Researcher · Nanjing University · Peking University +1
Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Sep 14, 2026Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma +1RL for Code Generation Offline RL
TU Darmstadt · Hessian Center for Artificial Intelligence, Darmstadt, Germany · National Research Center for Applied Cybersecurity ATHENE
Entropy-Regularized Rank-Masked Policy Optimization for Test-Time Reinforcement Learning in Code Generation
Sep 8, 2026Jiacheng Xu, Feng Chen, Xiuneng Xu +1RL for Code Generation Policy Optimization
Nanyang Technological University, Singapore
ExecCritic: Learn to Test, Test to Improve for Coding Agents
Sep 8, 2026Leitian Tao, Baolin Peng, Haorui Wang +7RL for Code Generation Software Engineering Agents
University of Wisconsin–Madison · Microsoft Research · Georgia Tech
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
Sep 7, 2026Minseon Kim, Zhengyan Shi, Emiliano Penaloza +14RL for Code Generation Software Engineering Agents
Froggy Team – Microsoft Research Montréal
What Does Multi-Harness RL Learn? Credit Assignment and Portability in Coding Agents
Sep 7, 2026Chenqian Le, Jiayi Cheng, Qijia He +3RL for Code Generation Group Relative Policy Optimization
New York University · University of Washington · University of Southern California +2
Train What You Deploy:Token-Faithful Post-Training of a Production Coding
Sep 7, 2026Cheng Li, Jiexiong Liu, Yixuan Chen +1RL for Code Generation Proximal Policy Optimization
KunlunMeta
DiffPDE: Masked Diffusion Language Models as PDE Solver
Aug 31, 2026Wenxuan Guo, Yuyang Hong, Lubin Fan +4RL for Code Generation Masked Diffusion Models
School of Artificial Intelligence, University of Chinese Academy of Sciences · MAIS, Institute of Automation, Chinese Academy of Sciences · Independent Researcher
Rubric-to-Code Credit Assignment for Reinforcement Learning
Aug 28, 2026Rui Jin, Jikai Chen, Yihan Chen +6RL for Code Generation Code Generation
Inclusion AI, Ant Group
CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
Aug 10, 2026Hejia Zhang, Sheng Lu, Zhongming Yu +3RL for Code Generation Automated Test Generation
1UC San Diego · 2Georgia Institute of Technology · 3NVIDIA
DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training
Aug 7, 2026Xucong Wang, Zhe Zhao, Liheng Yu +3RL for Code Generation Credit Assignment in RL
University of Science and Technology of China (USTC) · Stanford University · Tongji University +1
WebGrader: Training LLMs for Web Development with Self-Evolving Programmatic Grader
Aug 6, 2026Boshui Chen, Huiping Liu, Shaolei ZhangRL for Code Generation Automated Software Testing
Beijing Institute of Technology, China · Beijing Jiaotong University, China · Renmin University of China, China
RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
Aug 6, 2026Shuhao Yan, Changhao He, Peng Hu +1RL for Code Generation Code Generation
College of Computer Science, Sichuan University, Chengdu, China · School of Artificial Intelligence, Sichuan University, Chengdu, China · National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, Chengdu, China
LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation
Aug 3, 2026Tankun Li, Zhi Chen, Yaohua TangRL for Code Generation GPU Kernel Generation
Moore Threads AI
TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation
Aug 1, 2026Aofan Liu, Jingxiang Meng, Fangxin Liu +1RL for Code Generation Reward Shaping
∗These authors contributed equally. · †Corresponding author.
RLPF: Reinforcement Learning from Performance Feedback for Code Generation
Jul 29, 2026Huihao Jing, Haozhe Cui, Wenbin Hu +9RL for Code Generation Reinforcement Learning
The Hong Kong University of Science and Technology (HKUST)
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning
Jul 29, 2026Shuhang Wang, Ziming Li, Hui ChengRL for Code Generation Reinforcement Learning
Reinforcement Learning for Code Optimization
Jul 28, 2026Pierre Chambon, Kunhao Zheng, Juliette Decugis +2RL for Code Generation Reinforcement Learning
FAIR at Meta · Inria · Université Paris Dauphine
Learning as Reasoning Unfolds: Progressive Rollout Allocation for Efficient Reinforcement Learning
Jul 24, 2026Heyang Jiang, Henry Liu, Baharan MirzasoleimanRL for Code Generation RL for Language Model Reasoning
University of California, Los Angeles
Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning
Jul 24, 2026Shujin Wu, Cheng Qian, Xiusi Chen +1RL for Code Generation LLM Self-Refinement
University of Illinois Urbana-Champaign

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

BoT-GRPO: Efficient Process-Reward RL for Reasoning via Bag-of-Token Aggregation

Yingxiang Yang, Weihang Xiao, Zhunxuan Wang, Joshua Flashner+1

Amazon AGI · Virginia Tech

RL for Language Model Reasoning Group Relative Policy Optimization Efficient Language Model Reasoning Process Reward Models RL for Code Generation

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Reinforcement learning is now central to eliciting reasoning in large language models, while in the popular algorithm Group Relative Policy Optimization (GRPO) every token in a rollout receives the same advantage. We ask how to make process supervision efficient: accelerating convergence and improving final quality without the cost of value networks. We propose Bag-of-Tokens Group Relative Policy Optimization (BoT-GRPO), which extends GRPO to token-level reward models through a length-invariant "bag of tokens" aggregation: it collects all token-level rewards across rollouts, weights each by the inverse of its source sequence length, and computes per-token advantages relative to weighted group statistics. BoT-GRPO is critic-free, and is a drop-in replacement wherever GRPO is used when token-level reward is available. On React front-end code generation, BoT-GRPO reaches

80\%

compile rate up to

1.9\times

faster than GRPO and converges faster than modern GRPO variants (GSPO, DAPO, PURE) while reaching higher final compile and VLM-judged win rates. On a second task, AIME mathematical reasoning, BoT-GRPO delivers absolute Pass@

k

gains up to

8.1\%

over GRPO in half the steps. For both tasks we compare the algorithm's performance on reasoning vs. non-reasoning base-model families (Qwen2.5-3B, SmolLM3-3B, Phi-4-mini-reasoning). Our experiments also yield a practical recipe for the reward model itself: reward stability matters more than richness: clean, bounded, stable fine-grained signals consistently accelerate learning where noisier alternatives stall.

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