StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation

StarCoder: A State-of-the-Art LLM for Code

Creating a Coding Assistant with StarCoder

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

arXiv:2606. 28998v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards.

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback

arXiv:2507. 22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation.

Towards Functional Correctness of Large Code Models with Selective Generation

arXiv:2505. 13553v3 Announce Type: replace-cross Abstract: The hallucination of code generation models hinders their applicability to systems requiring higher safety standards.

Instruction Alignment for Binary Code Representation Learning

arXiv:2608. 11766v1 Announce Type: cross Abstract: Binary code representation learning is a fundamental problem in software security and reverse engineering.

StarCoder2 and The Stack v2

ExeCRE: Execution-Consistency Guided Reliability Estimation for Self-Correcting Code Generation

arXiv:2608. 04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated algorithms or complex implementations.

Functional and Secure Code Generation with Task Vectors

arXiv:2607. 07881v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities.

BigCodeArena: Judging code generations end to end with code executions

CommitLLM: A Fine-Tuned Pipeline for Git Commit Message Generation

Developers frequently write uninformative git commit messages such as "fix" or "update stuff", degrading the value of version-control history for code review, debugging, and onboarding. We present CommitLLM, a three-stage pipeline that generates concise, Conventional Commits-compliant messages from code diffs using a fine-tuned small language model.

Route-Align-Verify for Functional Correctness in Code Generation

arXiv:2608. 03341v1 Announce Type: cross Abstract: Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially for heterogeneous programming tasks where a single prompting strategy and a single directly generated output are often insufficient.