finetuning
This skill should be used when picking or diagnosing a training move (SFT, LoRA, DPO/KTO/ORPO, RFT, GRPO/PPO/RLOO, RLHF), or when the user mentions fine-tuning, post-training, training recipe, reward design, or weight updates. Decision tree by reward shape, smoke-run gate, three failure diagnostics, five false-progress patterns. Provider recipes and I/O contract in references/.
npx skills add evo-hq/evo --skill finetuning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Finetuning Priors, not rules. Only firm guardrails: held-out eval you never train on, no leakage, trust evo's recorded numbers over the run's self-report. Override anything else against the gate. ## Pick the technique by reward shape Decide on the reward first, technique second. Choosing the comfortable technique over the matching one is the most common failure. | Reward shape | Technique | |---|---| | Verifiable (exact match, unit tests, parser-decidable) | **RL** (GRPO / RLOO / PPO) — reward includes format, so the model learns to emit verifier-acceptable shape | | Preference pairs (chosen vs rejected) | **DPO / KTO / ORPO** — cheaper than full RL, no rollouts | | Demonstrations only (curated traces, chat data) | **SFT** — install format/tone/capability the base lacks | | Have a scorer + want SFT stability | **RFT** — sample, filter by reward, SFT on survivors | "SFT-then-RL" is not a law. For a competent base model on a verifiable benchmark, RL-from-base often beats SFT-then-RL end-to-end. ## Research the literature before the first commit The decision tree above is the structural prior. The empirical answer for *this* model on *this* benchmark usually has a recent paper, blog
- Pick the technique by reward shape
- Research the literature before the first commit
- Before committing the budget: smoke-run
- Long training: checkpoint, mid-eval, early-stop in-script
- Cap retries at training scale
- Four diagnostics
- What never counts as progress
- Surviving session compaction
- Numbers that matter (in order)
- Stream training metrics live
- Warm-start from a parent / prior checkpoint
- Cache expensive intermediates
- References
evo config set max-attempts 1
What does the finetuning skill do?
This skill should be used when picking or diagnosing a training move (SFT, LoRA, DPO/KTO/ORPO, RFT, GRPO/PPO/RLOO, RLHF), or when the user mentions fine-tuning, post-training, training recipe, reward design, or weight updates. Decision tree by reward shape, smoke-run gate, three failure diagnostics, five false-progress patterns. Provider recipes and I/O contract in references/.
How do I install it?
Run `npx skills add evo-hq/evo --skill finetuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From evo-hq/evo, a repository with 1,359 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.