finetuning-method-selection – Other skill for AI agents

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.

• Stable, dense domain knowledge: this is

where continued pretraining (CPT) enters, sized

by how much domain text exists:

| Domain text volume | Route |

|---|---|

| <10MB | RAG only |

| 10MB–500MB | RAG + fine-tune |

| 500MB–10GB | CPT, then SFT |

| >10GB | CPT required |

CPT learning rate ≈ **10% of the pretraining

LR**. CPT is guidance-only in this plugin —

sizing and LR guidance live here, but this

plugin does not execute a CPT run.

Method Router

Once the off-ramps are ruled out, this is the

full decision tree (verbatim from the research

this plugin is built on):

New FACTS?  volatile → RAG | stable+dense → CPT (LR ~10% of pretrain) → SFT
New BEHAVIOR? shifting → prompt-engineering | stable:
  demos → SFT (LoRA/QLoRA, all-linear, α=2r)
  preference pairs → DPO (SimPO if length-bias, ORPO if memory-bound)
  unpaired 👍/👎 → KTO
  verifiable success → RLVR + GRPO (DAPO/GSPO/Dr.GRPO per failure mode)
Deploy: FP8 (Hopper+) | NVFP4 (Blackwell scale) | AWQ (older) | GGUF+imatrix (edge)
BEFORE ANY OF THIS: the eval harness must exist first.

Read the tree top-down: answer "new facts or new

behavior," then follow the branch that matches

the data shape in hand (demos, preference pairs,

thumbs up/down, or verifiable success/failure).

The data shape picks the method — not the other

way around.

Worked Routing Examples

• *"Users want the assistant to follow our

support macros exactly."* Behavior is stable

and demonstrable from transcripts → demos →

SFT.

• *"We have pairs of good/bad responses from

reviewer thumbs-up/down, unpaired."* → unpaired

signal → KTO, not DPO (DPO needs paired

preferences).

• *"The model can already solve some of these

math problems and we can grade correctness

automatically."* → verifiable success signal →

GRPO+RLVR, and only after confirming the

model succeeds at least sometimes (see Key

Routing Facts below).

• *"We want the model to know this week's

pricing page."* → volatile facts → RAG, no

training run at all.

Key Routing Facts

• Loss-function choice is low-leverage. A

240-H100-run study found method choice worth

~1 percentage point versus ~50 points for model

scale, and zero of 20 DPO variants beat vanilla

DPO. Don't spend a routing decision agonizing

over DPO-variant selection — spend it on

getting the data shape and scale right.

• **DPO is for taste, GRPO+RLVR is for

reasoning.** Preference pairs that encode a

subjective judgment (tone, style, "which answer

is better") route to DPO. Tasks with a

verifiable pass/fail signal (math, code, tool

calls) route to GRPO+RLVR instead.

• **RL is not the fix for a model that never

succeeds.** GRPO and other RL methods sharpen

an existing capability — they don't teach one

from zero. If the model doesn't yet understand

the task or output format, run SFT first; only

bring in RL once the model succeeds at least

sometimes.

Common Routing Mistakes

• Reaching for fine-tuning to fix facts that

change weekly — that's a RAG problem, and

fine-tuning will just go stale faster than the

source data does.

• Picking a DPO variant before checking whether

the actual bottleneck is data quality or model

scale — variant choice is the ~1pp lever, not

the ~50pp one.

• Starting an RL run on a model that fails every

rollout — route to SFT first so RL has

something to sharpen.

• Treating CPT as the default for "the model

doesn't know our domain" — check the data

volume thresholds first; under 500MB, RAG or

RAG+fine-tune iterates faster than a CPT run.

Model Selection

Base-model choice is size-class first, family

second, and it goes stale fast — so it lives in

exactly one place: references/model-catalog.md.

That file is the only place in this plugin (and

in the DGX Spark ops plugin) that names a base

model family. Neither this skill nor

references/memory-math.md names one; both

describe models by size class only (for example,

"8B-class LoRA," not a model name).

The catalog is dated on purpose — model rankings

turn over quarterly. It carries a "last verified"

date and a refresh checklist. Before trusting a

row, check that date; if stale, work the refresh

checklist in the catalog before recommending a

model from it.

**Precedence when the catalog and a method skill

disagree:** the catalog's per-row Notes column

states hardware/size-class *feasibility*, not a

method recommendation — lora-qlora-recipes's

LoRA vs QLoRA vs Full FT table (routed by task

shape) governs the actual method choice.

Memory Feasibility

Before committing to a method, size it: total

memory ≈ **params × dtype bytes + optimizer

state + gradients + activations**. Work each

term for the chosen dtype and method (full

fine-tune, LoRA, or QLoRA) — worked worksheets

and size-class examples live in

references/memory-math.md.

On DGX Spark specifically, unified-memory

behavior breaks the naive estimate (transient

load peaks, nvidia-smi underreporting, thermal

throttling on long runs). Once the

dgx-spark-ops plugin is installed, defer

Spark-specific feasibility calls to its

spark-memory-thermal-ops skill rather than

re-deriving them here.

Related Skills

Once this skill has picked a method, hand off to

the skill that executes it:

• lora-qlora-recipes — SFT via LoRA/QLoRA
• preference-optimization — DPO, ORPO, KTO
• grpo-rlvr-training — GRPO with verifiable

rewards

No method is selected before the eval harness

exists — see eval-harness-first.