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Review regressions before expanding the test." ], "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.29421", "title": "Rufus-Air: An Open LLM Post-Training Recipe", "summary": "It replaces ad hoc single-stage fine-tuning with a documented serial pipeline of SFT followed by staged RL ordered by reward verifiability, ending in judge-based RLHF. The cost is substantial: post-training a 106B-A12B MoE model across eight RL stages requires multi-node GPU infrastructure, orchestration complexity, and compute budgets far beyond a single 24 GB GPU. 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