Unlearn — remove a capability

Remove a capability from a finished model using optimizer steps on forget/retain data.

flowchart LR
    subgraph RECOVER[Recover — training-free]
        A1[Finished model] --> B1[Weight-patching function<br/>e.g. RESTA · C-Θ · LoX]
        B1 --> C1[Patched weights<br/>no training run]
    end
    subgraph UNLEARN[Unlearn — forget-set training]
        A2[Finished model] --> B2[Training loop<br/>optimizer steps on forget + retain]
        A3[Forget set] --> B2
        A4[Retain set] --> B2
        B2 --> C2[Unlearned model<br/>new checkpoint]
    end

Input contract

A finished model + a forget set + a retain set. The forget set defines what to remove; the retain set preserves everything else. Unlike Recover, Unlearn trains: it runs optimizer steps.

Quick example

from safetune.runner import unlearn

trainer = unlearn.RMUTrainer(model)
trainer.unlearn(forget=forget_batches, retain=retain_batches)

Data format: forget and retain are iterables of tokenized batches — dicts with input_ids, attention_mask, and labels. For a quick start, unlearn.load_unlearn_data(model_id) returns a (forget, retain) pair. FLAT and SimDPO train on refusal/harmful preference pairs; pass raw forget batches and they build the pairs for you (see their pages).

Catalog of alternatives

Method	Mechanism	Guide
RMU	representation misdirection — steers harmful hidden states to random anchors	RMU
NPO	negative preference optimization — sigmoid-bounded NLL on forget set	NPO
GradientAscent / GradDiff	gradient ascent on forget set (+ KL preservation on retain)	Gradient Ascent
FLATTrainer	f-divergence variational bound, no reference model needed	FLAT
SimDPOTrainer	SimDPO-style unlearning, reference-free	SimDPO