Expired60% confidenceFactExact time
The training paradigm for large code models typically consists of three stages: pre-training on massive code corpora, instruction fine-tuning to enable the model to understand programming tasks, and optimizing output quality through RLHF.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
ByteDance Codex Chinese Manual: An In-Depth Guide to AI-Powered Programming
bilibili筝浮6/13/2026
Related Claims
UnverifiedMainstream code large models all adopt a two-stage strategy of "pretraining + instruction fine-tuning," with the second stage using alignment techniques such as RLHF or DPO.84% similarVerifiedPre-training on code not only improves performance on programming tasks but also significantly enhances a model's logical reasoning capabilities, and has become standard practice for top-tier foundation models.79% similarPartially Verified大语言模型生成代码的底层机制是基于海量训练语料的条件概率预测，模型并不真正理解代码逻辑78% similarUnverifiedIt is recommended that developers familiarize themselves with base models such as GLM, Mistral, Llama, DeepSeek, and MoE in advance to help understand the code logic of fine-tuning training.77% similarVerifiedTraining strategies for mathematical LLMs include continued pre-training on large-scale mathematical corpora on top of general pre-training, followed by supervised fine-tuning with chain-of-thought reasoning data, combined with reinforcement learning using process reward models.76% similar
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