Rufus-Air: An Open LLM Post-Training Recipe

You already need task-specific training and want to reduce its memory or compute cost.

What you would change

Change only the training technique on a small controlled run after checking prerequisites.

Change only the training technique on a small controlled run after checking prerequisites.

Compare against

Your current training recipe with the same model, data split, and evaluation.

1. Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.
2. Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.
3. Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.
4. Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test.

What to measure

Held-out quality, peak memory, training time, and full compute cost to the target quality.

When to keep it

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.

recommendation

watch

research area

efficient training

technical change

Multi-stage post-training recipe (SFT plus staged RL)

published

2026-09-24

The recipe's design principles (difficulty filtering, ordering stages from verifiable to judge-based rewards, strong SFT as a capability floor) are transferable insights, but the method itself targets a 106B model and cannot be executed on the reader's constrained infrastructure.

1. claim 01Paperraft inference
   Read the brief interpretation

   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. What can fail is reward reliability at softer stages, difficulty miscalibration of RL prompts, and dependence on specific base-model behavior and engineering choices that do not transfer to smaller models.

   This is a Paperraft inference. It is not pinned to a verified PDF passage.

   Inspect the source
2. claim 02Paperraft inference

   Improves over the official GLM-4.5-Air post-trained release and is reported competitive with similarly sized open models; no numeric margin is given in the abstract.

   This is a Paperraft inference. It is not pinned to a verified PDF passage.

   Inspect the source