CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL

• URL: http://arxiv.org/abs/2508.05242v1
• Date: Thu, 07 Aug 2025 10:31:24 GMT
• Title: CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL
• Abstract summary: Code large language models (LLMs) have become indispensable tools for building efficient and automated coding pipelines.<n>Existing models are typically post-trained using reinforcement learning (RL) from general-purpose LLMs using "human instruction-final answer" pairs.<n>We propose CodeBoost, a framework that enhances code LLMs purely from code snippets, without relying on human-annotated instructions.
• Score: 28.43882967593511
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code large language models (LLMs) have become indispensable tools for building efficient and automated coding pipelines. Existing models are typically post-trained using reinforcement learning (RL) from general-purpose LLMs using "human instruction-final answer" pairs, where the instructions are usually from manual annotations. However, collecting high-quality coding instructions is both labor-intensive and difficult to scale. On the other hand, code snippets are abundantly available from various sources. This imbalance presents a major bottleneck in instruction-based post-training. We propose CodeBoost, a post-training framework that enhances code LLMs purely from code snippets, without relying on human-annotated instructions. CodeBoost introduces the following key components: (1) maximum-clique curation, which selects a representative and diverse training corpus from code; (2) bi-directional prediction, which enables the model to learn from both forward and backward prediction objectives; (3) error-aware prediction, which incorporates learning signals from both correct and incorrect outputs; (4) heterogeneous augmentation, which diversifies the training distribution to enrich code semantics; and (5) heterogeneous rewarding, which guides model learning through multiple reward types including format correctness and execution feedback from both successes and failures. Extensive experiments across several code LLMs and benchmarks verify that CodeBoost consistently improves performance, demonstrating its effectiveness as a scalable and effective training pipeline.

Related papers

• Mitigating Exploration Bias in RL for Multi-Instruction Following [45.91710862879366]
  We find that existing training recipes suffer from exploration bias towards easy instructions when the training data has multiple instructions in a prompt.<n>This bias is caused by two main reasons: 1) the policy model's initial ability to satisfy hard instructions is too low to trigger successful exploration during RL training, so the optimization is biased towards easy instructions.<n>We propose two metrics to measure the exploration bias in instruction following and then introduce a two-stage framework to alleviate it.
  arXiv  Detail & Related papers  (2026-08-24T21:19:46Z)
• Instruction Alignment for Binary Code Representation Learning [6.28246120991306]
  We propose to leverage instruction alignment knowledge to improve binary code representation learning.<n>Our preliminary study reveals that models finetuned for function-level binary code similarity exhibit substantially better instruction alignment than their pre-trained model.
  arXiv  Detail & Related papers  (2026-08-12T08:07:26Z)
• Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning [17.688379880523566]
  Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs.<n>Existing work has verified the effectiveness of reinforcement learning on the task.<n>We propose CodeThinker, a consistency-driven reinforcement learning framework for code reasoning.
  arXiv  Detail & Related papers  (2026-05-18T07:12:44Z)
• Readability-Robust Code Summarization via Meta Curriculum Learning [53.44612630063336]
  In the real world, code is often poorly structured or obfuscated, significantly degrading model performance.<n>We propose RoFTCodeSum, a novel fine-tuning method that enhances the robustness of code summarization against poorly readable code.
  arXiv  Detail & Related papers  (2026-01-09T02:38:24Z)
• CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment [98.87395842351627]
  Large Language Models (LLMs) excel at code generation by learning from vast code corpora.<n>A fundamental semantic gap remains between their training on textual patterns and the goal of functional correctness.<n>We propose CodeRL+, a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation.
  arXiv  Detail & Related papers  (2025-10-21T09:48:06Z)
• IFEvalCode: Controlled Code Generation [69.28317223249358]
  The paper introduces forward and backward constraints generation to improve the instruction-following capabilities of Code LLMs.<n>The authors present IFEvalCode, a multilingual benchmark comprising 1.6K test samples across seven programming languages.
  arXiv  Detail & Related papers  (2025-07-30T08:08:48Z)
• ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding [60.37988508851391]
  Language models (LMs) have become a staple of the code-writing toolbox.<n>Research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better disentangling between syntax and semantics, has been noticeably sparse.<n>In this work, we examine grounding on obfuscated code as a means of helping Code-LMs look beyond the surface-form syntax and enhance their pre-training sample efficiency.
  arXiv  Detail & Related papers  (2025-03-27T23:08:53Z)
• Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
  We propose a pretraining strategy to enhance the integration of natural language and coding capabilities. The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
  arXiv  Detail & Related papers  (2024-11-06T10:28:46Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.<n>Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs.<n>We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning [36.78560777629329]
  We introduce a diverse instruction model (DolphCoder) with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2024-02-14T12:34:58Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• Coarse-Tuning Models of Code with Reinforcement Learning Feedback [0.0]
  Large Language Models (LLMs) pre-trained on code have emerged as the dominant approach to program synthesis. We propose RLCF, that further trains a pre-trained LLM via reinforcement learning, using feedback from a grounding function that scores the quality of the code.
  arXiv  Detail & Related papers  (2023-05-25T22:09:08Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)
• CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation [36.47905744758698]
  We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Our model employs a unified framework to seamlessly support both code understanding and generation tasks and allows for multi-task learning.
  arXiv  Detail & Related papers  (2021-09-02T12:21:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.