# Code Generation with LLMs: Research Digest

> Foundational work covers surveys, planning, fine-tuning and test-based evaluation. Recent preprints move toward verifier-grounded reinforcement learning, algorithmic diversity benchmarks, and training and decoding for diffusion language models.

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- **Updated:** 2026-10-16
- **Category:** scholar
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## Key data

- **Field:** code generation large language models
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-16

Foundational work covers surveys, planning, fine-tuning and test-based evaluation. Recent preprints move toward verifier-grounded reinforcement learning, algorithmic diversity benchmarks, and training and decoding for diffusion language models.

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## Sources

1. [A survey on large language models for code generation](https://dl.acm.org/doi/abs/10.1145/3747588)
2. [Planning with large language models for code generation](https://arxiv.org/abs/2303.05510)
3. [Self-planning code generation with large language models](https://dl.acm.org/doi/abs/10.1145/3672456)
4. [Large language models for code generation: A comprehensive survey of challenges, techniques, evaluation, and applications](https://arxiv.org/abs/2503.01245)
5. [Usage of large language model for code generation tasks: A review](https://link.springer.com/article/10.1007/s42979-025-04241-5)
6. [Codet5+: Open code large language models for code understanding and generation](https://aclanthology.org/2023.emnlp-main.68/)
7. [Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation](https://proceedings.neurips.cc/paper_files/paper/2023/hash/43e9d647ccd3e4b7b5baab53f0368686-Abstract-Conference.html)
8. [On the effectiveness of large language models in domain-specific code generation](https://dl.acm.org/doi/abs/10.1145/3697012)
9. [Exploring parameter-efficient fine-tuning techniques for code generation with large language models](https://dl.acm.org/doi/abs/10.1145/3714461)
10. [Large language model-aware in-context learning for code generation](https://dl.acm.org/doi/abs/10.1145/3715908)
11. [Learning from Interaction Experience via Localized Context Distillation](https://arxiv.org/abs/2610.21427v1)
12. [Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling](https://arxiv.org/abs/2610.20497v2)
13. [PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference](https://arxiv.org/abs/2610.19605v1)
14. [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://arxiv.org/abs/2610.19301v1)
15. [Verifier-Grounded Agentic RL for Provably Correct Code](https://arxiv.org/abs/2610.19298v1)
16. [DivAlgo: A systematic evaluation of algorithmic diversity for code generation models](https://arxiv.org/abs/2610.19157v1)
17. [Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models](https://arxiv.org/abs/2610.18001v1)
18. [HaloBlock: Faster and Better Decoding for Diffusion Large Language Models](https://arxiv.org/abs/2610.17948v1)

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