From 90b5199ed991cd55d61fc800ccc0ee13da270127 Mon Sep 17 00:00:00 2001 From: wassname <1103714+wassname@users.noreply.github.com> Date: Mon, 15 Jun 2026 07:05:45 +0800 Subject: [PATCH] README: AntiPaSTO family GSM8K results (5 variants, r=256) Replace the stale single AntiPaSTO row (was 35.8K params from the removed rotation version, described block-Cayley which no longer exists) with the real 5000-step Qwen3-0.6B numbers and a family breakdown: corda 61.9% 14.3K (best: covariance-oriented basis) plain 61.4% 14.3K rot 61.4% 35.8K (the rotation this replaces) ablate 61.0% 14.4K arrow 60.5% 17.5K Headline: ~320x fewer trainable params than LoRA at ~97% of its accuracy. Rotation buys nothing (rot matches plain to 3 s.f. at 2.5x params, +20% wall-time, plus a per-forward Cayley solve), confirming the drop. Co-Authored-By: Claudypoo --- README.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7bb142c..9966e68 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe | [PiSSA](https://arxiv.org/abs/2404.02948) | no | 63.2% | 4.59M | 11.3 | | [DoRA](https://arxiv.org/abs/2402.09353) | no | 62.4% | 4.67M | 11.3 | | [DeLoRA](https://arxiv.org/abs/2503.18225) | yes | 61.5% | 4.59M | 11.3 | -| [AntiPaSTO](https://arxiv.org/abs/2601.07473) | no | 61.4% | 35.8K | 11.5 | +| [AntiPaSTO](https://arxiv.org/abs/2601.07473) | no | 61.4% | 14.3K | 11.3 | | [IA3-FF](https://arxiv.org/pdf/2205.05638) | yes | 61.4% | 86K | 11.4 | | [EVA](https://arxiv.org/abs/2410.07170) | no | 60.3% | 4.59M | 11.3 | | [IA3](https://arxiv.org/pdf/2205.05638) | yes | 60.0% | 57K | 11.4 | @@ -61,11 +61,23 @@ just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe Params = trainable adapter params. Peak GPU = peak CUDA memory during train+eval (logged from this run onward; older runs predate the column). -Setup: Qwen3-0.6B-Base, MetaMathQA train (5k steps, batch 4 = 20k samples unless noted), r=32, all q/v targets, GSM8K test (1319 examples). HRA used batch 2 (10k samples) due to memory. AntiPaSTO used r=256 (default for this variant). +Setup: Qwen3-0.6B-Base, MetaMathQA train (5k steps, batch 4 = 20k samples unless noted), r=32, all q/v targets, GSM8K test (1319 examples). HRA used batch 2 (10k samples) due to memory. The AntiPaSTO family used r=256 (default for these variants). Reference: PEFT reports LoRA at 49.0% on Llama-3.2-3B (different model, different sample count). Our numbers are not directly comparable but suggest the adapters work. -AntiPaSTO at 59.5% with 4.5K trainable params (1000x fewer than LoRA's 4.59M). It trains singular-value deltas + block-Cayley rotation within the SVD subspace, so it can rescale and reorient existing directions but not create new ones. Higher rank (r>32) or data-driven dimension selection (from antipasto3) may close the gap further. +### AntiPaSTO family + +AntiPaSTO learns a per-direction gain on the frozen top-r SVD basis (`S_eff = S * (1 + ELU(coeff*g))`), so it rescales existing singular directions rather than creating new ones, hence ~320x fewer trainable params than LoRA at ~97% of its accuracy. All variants share the diagonal gain; they differ only in the basis they steer in or the extra structure on the top directions. All have `base_grad_leaks=0` (the frozen residual weight gets no gradient). + +| Variant | GSM8K % | Params | Basis / extra structure | +| ------------------ | ------- | ------ | ------------------------------------------------------------------- | +| antipasto_corda | 61.9% | 14.3K | covariance-oriented input projector `P = Vh·C^{-1/2}` (best of family) | +| antipasto | 61.4% | 14.3K | plain weight-SVD basis, diagonal gain only | +| antipasto_rot | 61.4% | 35.8K | + block-Cayley rotation of the basis (the version this replaces) | +| antipasto_ablate | 61.0% | 14.4K | contractive output ablation `(I - α ĉĉᵀ)diag(S)`, can't amplify | +| antipasto_arrow | 60.5% | 17.5K | dense b×b mixing block on the top-b directions + diagonal tail | + +The rotation buys nothing here: `antipasto_rot` matches plain `antipasto` to 3 s.f. (61.4%) at 2.5x the params and +20% wall-time, while paying a per-forward Cayley solve. Dropping it (the current default) is free. CorDA's data-oriented basis is the only structure that helps on this capability task; the ablation/arrow cores are aimed at steering and suppression, where the diagonal-only basis can't reach off-axis behavior, so they don't pay off for raw GSM8K accuracy. ## Developer docs