Author

Boyuan Zhu

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#artificial intelligence Preprint Feb 2026

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models

This work proposes CodeScaler, a reward model designed to scale both reinforcement learning training and test-time inference for code generation, and surpasses existing reward models on RM-Bench not only in the code domain, but also in general and reasoning domains.

Xiao Zhu, Xinyu Zhou, Boyuan Zhu et al. · 2 citations

#natural language process... Jun 2026

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It

QK-Restore consistently restores long-context capability at zero training cost while preserving reasoning performance; for instance, on HypeNet-5B it improves S3@256K from $65.4\%$ to $76.4\%$ while maintaining strong reasoning performance.

Xinyu Zhou, Boyuan Zhu, Yi Xu et al. · arXiv.org · 0 citations