DeepSeek V4 Post-Training Breakdown
heghbalz · x · 2026-07-15
This post links to a technical breakdown of the DeepSeek V4 post-training scheme. After reading through the technical report, the author focused on analyzing the structure and trade-offs of its post-training recipe.
Key takeaways include:
- The document does not explicitly mention traditional preference optimization pipelines like RLHF / DPO;
- Training relies more heavily on RLVR and multi-teacher on-policy distillation;
- For domains with verifiable answers (math, competitive programming), specialized models are trained separately using rule-based verifiable rewards;
- For domains that are hard to verify (writing, agentic behavior, subjective tasks), a Generative Reward Model (GRM) is utilized.
The quoted section adds a common difficulty in such multi-domain post-training: simultaneously excelling in math, code, and instruction-following is difficult. When one capability improves, another tends to regress, requiring methods like MOPD to mitigate the "seesaw effect."
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