Agent skill · research science · wshobson
finetuning-method-selection
Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.
Why this skill is useful
Provides a structured decision-making framework for selecting fine-tuning methods based on specific scenarios, which the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
Fine-Tuning Method Selection This is the router skill for the fine-tuning lifecycle: it decides whether fine-tuning is the right tool at all, and if so, which method and which base-model size class. Every other skill in this plugin assumes this routing already happened — start here before opening lora-qlora-recipes, preference-optimization, or grpo-rlvr-training. When to Use This Skill Starting any fine-tuning effort, before a framework or base model has been chosen. Unsure whether RAG or prompt engineering would solve the problem more cheaply than training. Choosing between preference optimization (DPO family) and a reinforcement method (GRPO/RLVR) for the same underlying task. Sizing a candidate model/method combination before committing to a run. Quick Reference Situation Route --- --- Facts change often (prices, docs, news) RAG, not fine-tuning Desired behavior still being figured out Prompt engineering Stable domain knowledge, ≥500MB text CPT then SFT — see Off-Ramps First Have input/output demonstrations SFT — see lora-qlora-recipes Have preference pairs or thumbs-up/down DPO/ORPO/KTO — see preference-optimization Have a verifiable pass/fail signal GRPO+RLVR — see grpo-rlvr-training No eval harness yet Stop — see eval-harness-first Off-Ramps First Most requests that sound like "fine-tune this" are served better and cheaper elsewhere. Check these off-ramps before opening a training run: Knowledge-bound and volatile (the gap is facts that change — prices, docs, current events): route to RAG, not fine-tuning. A fine-tuned model bakes in a snapshot; volatile facts go stale immediately. Behavior-bound and shifting (the desired behavior is still being figured out, or changes per request): route to prompt engineering. Fine-tuning locks in a behavior; don't lock in one that hasn't stabilized yet. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/finetuning-method-selection