Wu Shixiong's Large Model Academy
Dec 10, 2025 · Artificial Intelligence
Why RLHF Success Relies on Data Engineering, Not Just Model Tricks
The article explains that the real difficulty of RLHF lies in designing and curating high‑quality preference data, building robust reward models through bad‑case rewriting, human‑in‑the‑loop labeling, and inference‑based reward modeling, while algorithmic details like PPO are secondary concerns.
Data engineeringGRPOLarge Language Models
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