COVERT generates verifiable synthetic tool-use environments for RL by validated trajectory synthesis and oracle-preserving augmentations, improving tool-use accuracy on BFCL v3 and ACEBench while remaining complementary to SFT.
Enhancing code llms with reinforcement learning in code generation: A survey
10 Pith papers cite this work. Polarity classification is still indexing.
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abstract
With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques.
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SCRL adds selective positive pseudo-labeling and entropy-gated negative pseudo-labeling to test-time RL, reducing noise from weak consensus and improving LLM reasoning on benchmarks.
PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
In OSS repos that commit AI chat logs, AI use is heavier in smaller, less collaborative projects; chats almost always precede commits, quality signals do not broadly worsen, and developers trust their own AI code more than others'.
CodePivot uses Python as a pivot language plus an Aggressive-Partial-Functional RL reward to train a 7B model that outperforms much larger LLMs on multilingual code transpilation without parallel corpora.
RIDER improves RNA 3D structural similarity by over 100% using RL-guided diffusion and discovers non-native sequence designs.
Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.
Survey mapping RL techniques onto LLM training and highlighting gaps in value-based, off-policy, and bootstrapping methods.
BV-Blend blends prompt-local and semantic-cluster historical reward statistics via SEM-derived weights to stabilize critic-free RL advantage estimation.
The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.
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Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning
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What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time
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PerfCoder: Large Language Models for Interpretable Code Performance Optimization
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From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software
In OSS repos that commit AI chat logs, AI use is heavier in smaller, less collaborative projects; chats almost always precede commits, quality signals do not broadly worsen, and developers trust their own AI code more than others'.
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CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora
CodePivot uses Python as a pivot language plus an Aggressive-Partial-Functional RL reward to train a 7B model that outperforms much larger LLMs on multilingual code transpilation without parallel corpora.
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RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion
RIDER improves RNA 3D structural similarity by over 100% using RL-guided diffusion and discovers non-native sequence designs.
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Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Survey mapping RL techniques onto LLM training and highlighting gaps in value-based, off-policy, and bootstrapping methods.
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