# Code Generation with LLMs: Research Digest

> 10 foundational papers with 364 combined citations, plus the latest arXiv preprints.

- **Canonical:** https://agentflare.org/scholar/code-generation-with-llms-research-digest.html
- **Updated:** 2026-10-30
- **Category:** scholar
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## Key data

- **Field:** code generation large language models
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-30

10 foundational papers with 364 combined citations, plus the latest arXiv preprints.

_…full analysis and the complete dataset are available to agents for $0.02 — fetch `/scholar/code-generation-with-llms-research-digest.data.json` (HTTP 402)._

## Sources

1. [Aligning Requirement for Large Language Model's Code Generation](https://dl.acm.org/doi/abs/10.1145/3744916.3764572)
2. [Fine-tuning mistral 7b large language model for python query response and code generation: A parameter efficient approach](https://vfast.org/journals/index.php/VTCS/article/view/1885)
3. [A framework for training large language models for code generation via proximal policy optimization](https://i2.cs.hku.hk/~cwu/papers/gmsheng-NL2Code24.pdf)
4. [Where are large language models for code generation on github?](https://ui.adsabs.harvard.edu/abs/2024arXiv240619544Y/abstract)
5. [Revisiting chain-of-thought in code generation: Do language models need to learn reasoning before coding?](https://openreview.net/forum?id=wSZeQoJ1Vk)
6. [What Makes Effective Knowledge Distillation for Large Language Models in Code Generation?](https://ieeexplore.ieee.org/abstract/document/11710277/)
7. [Imperfect code generation: Uncovering weaknesses in automatic code generation by large language models](https://www.computer.org/csdl/proceedings-article/icse-companion/2024/050200a422/1XVHAMQKEta)
8. [Evaluating the impact of post-training quantization on large language models for code generation](https://dl.acm.org/doi/abs/10.1145/3794763.3794810)
9. [Quantizing large language models for code generation: A differentiated replication](https://ui.adsabs.harvard.edu/abs/2025arXiv250307103G/abstract)
10. [LLMs for science: Usage for code generation and data analysis](https://onlinelibrary.wiley.com/doi/abs/10.1002/smr.2723)
11. [Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards](https://arxiv.org/abs/2610.37041v1)
12. [HiLo-RD: Cross-Budget Self-Distillation for Masked Diffusion Language Models](https://arxiv.org/abs/2610.36901v1)
13. [CAMR: Compatibility-Aware Memory Replay for Continual Learning in Large Language Models](https://arxiv.org/abs/2610.36734v1)
14. [Benchmarking LLM Agents for Behaviorally Adequate Test Generation from Software Requirements](https://arxiv.org/abs/2610.36584v1)
15. [Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models](https://arxiv.org/abs/2610.35804v1)
16. [Transformer to GPT-5: Advancements, Limitations, and Future Directions in Large Language Model Design](https://arxiv.org/abs/2610.34281v1)
17. [Secure and Functional Code Generation with Self-Evaluation](https://arxiv.org/abs/2610.33450v1)
18. [Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping](https://arxiv.org/abs/2610.32492v1)

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