Rufus-Air paper: ordering post-training by reward reliability builds competitive open LLM
burny_tech · x · 2026-09-27
The Rufus-Air paper presents an open LLM post-training recipe that orders training stages by reward reliability — hard verifiable rewards first, softer judge signals later — producing a competitive open model with no new human annotation. For practitioners building custom models or domain agents, it offers a concrete blueprint covering reward design, prompt difficulty filtering, general/coding/search agent capabilities, and training stability across multiple RL stages.
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