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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Latest papers 89

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Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers
May 18, 2026Soheyl Massoudi, Gabriel Apaza, Milad Habibi +1RL for Code Generation LLM-Based Program Synthesis
ETH Zürich Zürich, Switzerland · University of Maryland College Park, MD, USA
Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation
May 16, 2026Shuyin Ouyang, Zhaozhi Qian, Faroq AL-Tam +2RL for Code Generation Diffusion Guidance
King’s College London · Elm Europe
Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
May 14, 2026Han Li, Jinyu Tian, Rili Feng +10RL for Code Generation Continual Learning for LLM Agents
Nanjing University · Independent Researcher · Tsinghua University
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
May 14, 2026Mengjie Ren, Jie Lou, Boxi Cao +6RL for Code Generation LLM Self-Correction
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Xiaohongshu Inc.
GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
May 14, 2026Noah FlynnRL for Code Generation Program Synthesis
University of California, Berkeley, CA, USA.
HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning
May 13, 2026Qingyun Zou, Feng Yu, Hongshi Tan +3RL for Code Generation Reward Modeling
National University of Singapore
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
May 13, 2026Zhongkai Yu, Yichen Lin, Chenyang Zhou +12RL for Code Generation Code Generation
UCSD · Columbia University
Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling
May 11, 2026Yizhu Jiao, Ruixiang Zhang, Richard Bai +3RL for Code Generation Test-Time Scaling
Apple · University of Illinois Urbana-Champaign
PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
May 8, 2026Mehmet IscanRL for Code Generation Execution-Guided Code Generation
PythaLab, Yildiz Technical University
Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair
May 8, 2026Jia Li, Yuxin Su, Ting Peng +3RL for Code Generation Reward Shaping
The Chinese University of Hong Kong · Sun Yat-sen University · Tencent
Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
May 7, 2026Yujia Chen, Yang Ye, Xiao Chu +2RL for Code Generation Reinforcement Learning
Harbin Institute of Technology, Shenzhen · Huawei Cloud Computing Technologies Co., Ltd.
Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime
May 6, 2026Tianshu Zhu, Wenyu Zhang, Xiaoying Zuo +8RL for Code Generation Reinforcement Learning
Baidu
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards
May 5, 2026Tianyang Han, Hengyu Shi, Junjie Hu +3RL for Code Generation Process Reward Models
1D4 Lab · 2Independent Researcher
Strategy-Aware Optimization Modeling with Reasoning LLMs
May 4, 2026Ruiqing Zhao, Fengzhi Li, Yuan Zuo +5RL for Code Generation LLM-Based Program Synthesis
School of Computer Science and Engineering, Beihang University, Beijing, China · JIUTIAN Research, Beijing, China · MIIT Key Laboratory of Data and Decision Intelligence, Beihang University, Beijing, China
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
May 1, 2026Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3RL for Code Generation Reinforcement Learning
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning
May 1, 2026Shouyu Yin, Zhao Tian, Junjie Chen +1RL for Code Generation Curriculum Learning
Tianjin University, Tianjin, China · Dalian Maritime University, Dalian, China
Do Prompt-Elicited Trajectories Reflect Training-Time Reward Hacking? A Systematic Study on Monitoring Trainig-Time Reward Hacking in Code Generation
Apr 26, 2026Lichen Li, Hengguang Zhou, Yijun Liang +2RL for Code Generation Reward Hacking
Peking University · University of California, Los Angeles · University of Maryland +2
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution
Apr 24, 2026Xiangxi Zheng, Kuang He, Jiayi Hu +6RL for Code Generation VLM Adaptation
Nanjing University · LLM Team, Shopee Pte. Ltd. · East China Normal University +2
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL
Apr 22, 2026Zhaofeng Wu, Shiqi Wang, Boya Peng +5Supervised Fine-Tuning RL for Code Generation
Meta Superintelligence Labs · MIT
WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
Apr 22, 2026Juyong Jiang, Chenglin Cai, Chansung Park +4RL for Code Generation RL for Language Models
The Hong Kong University of Science and Technology (Guangzhou) · Alibaba · Tongyi Lab, Alibaba Group +3
Training and Agentic Inference Strategies for LLM-based Manim Animation Generation
Apr 20, 2026Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle +2RL for Code Generation Text-to-Video Generation
Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom · Department of Physics and Mathematics, Nottingham Trent University, Nottingham, United Kingdom
MARS $^2$ : Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation
Apr 16, 2026Pengfei Li, Shijie Wang, Fangyuan Li +7RL for Code Generation Multi-Agent Collaboration
Shanghai Artificial Intelligence Laboratory · Shanghai Innovation Institute · Harbin Institute of Technology +1
An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
Apr 9, 2026Andreas Plesner, Francisco Guzmán, Anish AthalyeRL for Code Generation Reinforcement Learning
Handshake AI · ETH Zurich
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
Apr 3, 2026DeepReinforce Team, Xiaoya Li, Guoyin Wang +3RL for Code Generation Agentic RL
DeepReinforce Team
RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
Apr 1, 2026Shaopeng Fu, Xingxing Zhang, Li Dong +1RL for Code Generation LLM Self-Refinement
Work done during an internship at Microsoft Research · KAUST · Microsoft Research
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Feb 4, 2026Xiao Zhu, Xinyu Zhou, Boyu Zhu +5RL for Code Generation Reward Modeling
LARK, HKUST(GZ) · Kuaishou Technology · UCL +3
X-Coder: Advancing Competitive Programming with Synthetic Tasks, Solutions, and Tests
Jan 11, 2026Jie Wu, Haoling Li, Xin Zhang +8RL for Code Generation Synthetic Data Generation
Tsinghua University · Microsoft
ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search
Aug 27, 2025Sining Zhoubian, Dan Zhang, Jie TangRL for Code Generation RL for Language Model Reasoning
Department of Computer Science and Technology, Tsinghua University · Work done when interned at Z.ai. · School of Electronics and Computer Scinence, Univeristy of Southampton
Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
Aug 6, 2025Aleksander Boruch-Gruszecki, Yangtian Zi, Zixuan Wu +4RL for Code Generation Code Generation
1Northeastern University · University of Texas · 3Wellesley College

May 18, 2026, cs.LGJ/K move · Enter open · S save

Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers

Soheyl Massoudi, Gabriel Apaza, Milad Habibi, Mark Fuge

ETH Zürich Zürich, Switzerland · University of Maryland College Park, MD, USA

RL for Code Generation LLM-Based Program Synthesis Combinatorial Optimization Constrained RL RL for Combinatorial Optimization RL Fine-Tuning

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Large language models (LLMs) typically approach combinatorial optimization as an inference-time procedure, solving each instance separately through sampling, search, or repeated prompting. We ask whether reinforcement learning can instead shift part of this reasoning cost into the weights of a code LLM, so that the model synthesizes a reusable solver for an entire problem family. We study this question on Synergistic Dependency Selection (SDS), a controlled variant of constrained Quadratic Knapsack designed to expose a specific failure mode: local signals and strict feasibility constraints make greedy heuristics attractive but unreliable. Under identical scaffolding, Best-of-64 base-model sampling saturates at an approximately 28.7% gap to the global Virtual Best Solver (VBS); code audits show that the base model often retrieves Simulated Annealing templates but misimplements the Metropolis acceptance rule. We fine-tune Qwen2.5-Coder-14B-Instruct with Group Relative Policy Optimization (GRPO) using a feasibility-gated reward and light structural scaffolding. The resulting policy converges to a constraint-aware Simulated Annealing template in 99.8% of feasible SDS outputs, achieves a 5.0% gap to that VBS, and is 91 times cheaper in post-generation execution/search cost than cumulative Best-of-64 evaluation. A compile-once check shows that one best frozen solver per seed remains highly competitive when reused unchanged across the SDS test set, while an additional-domain evaluation on Job Shop Scheduling provides narrower but positive evidence that the scaffold transfers beyond SDS. Negative ablations reveal the limits of this recipe: standard stabilizers degrade performance, a soft feasibility gate fails, and results remain sensitive to reward normalization and domain-specific design choices.

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