feat: add ADAPT online reweighting weighter - #54
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Aug 9, 2026
Behaviour-preserving fixes plus the docs #58 left out. Default hyper-parameters and the solver math are deliberately untouched. - Use eval mode when extracting embeddings: _extract_embeddings forced model.train() before a no_grad forward, turning dropout on and adding noise to S and s. _compute_eval_target already used eval mode; this makes the two consistent and restores the original mode afterwards. No effect at the default lora_dropout 0.0. - Make the anchor direction u averageable: _compute_eval_target pulled a single eval batch, so at the default target_batch_size 1 u was one sample's embedding, not the 'eval set mean' its docstring claimed. Adds target_num_batches (default 1, unchanged); set 0 to average the whole eval set. The mean is sample-weighted so a short trailing batch no longer overcounts. - Drop the unused seed parameter: stored and exposed in components.yaml but never read, implying controllable randomness. - Document the method in skills/how_to_use.md (choices, algorithm table, examples tree, run command) and the Data Reweighting tables in both READMEs, mirroring what #54 did for ADAPT. - Add the missing train_step to the example config, matching loss.yaml and adapt.yaml. Verified: solver returns bit-identical weights at shipped defaults, target_num_batches=1 reproduces the previous single-batch u, model is left in train mode after embedding extraction, component still builds through the registry from the shipped components.yaml. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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What
This PR add ADAPT, an online, similarity-based sample reweighting method (ICLR 2026) - as a new
weighter.Changes
weighter/adapt_weighter.py-AdaptWeighter(@register_weighter('adapt'))weighter/init.py- register the weighterconfigs/components.yaml-adaptpresetexamples/train_lora/weighters/adapt.yaml- example configUsage