| # Reproduction — *Why Self-Training Helps and Hurts: Denoising vs. Signal Forgetting* |
|
|
| Independent reproduction of ICML 2026 paper **VnA5q5jXVz** (arXiv **2602.14029**), |
| Wu, Yang & Sun. No official code was released; everything here is written from the paper. |
|
|
| ## What is verified |
|
|
| | Claim | Content | Status | Evidence | |
| |---|---|---|---| |
| | 1 | Deterministic-equivalent recursion `R*_t = B*_t (forgetting, up) + V*_t (noise, down exp)` | verified | `claim1_decomposition.py`, `general_cov.py` (spiked Thm 3.2 **and** general Thm 4.2) | |
| | 2 | U-shaped risk & optimal early stopping, **strictly** under anisotropy (s>1) | verified | `claim2_ushape.py` (Fig 1b, Fig 3a) | |
| | 3 | Direction-dependent spectral filter: survival `(s/(s+tau))^{t+1}`, noise `(1+tau)^{-t}` | verified | `claim3_spectral.py` | |
| | 4 | iGCV consistently estimates risk & recovers t* with no validation set | verified | `claim4_igcv.py` | |
| | 5 | Trade-off persists for deep nets (ResNet-50 / CIFAR-10 self-distillation) | verified (GPU Job) | `cifar_selfdistill.py` (Fig 6a) | |
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|
| ## Layout |
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|
| - `src/linear_selftrain.py` — Algorithm 1 (ridgeless/ridge self-training), spiked Thm 3.2, |
| multi-spike Thm 3.6, **general** deterministic-equivalent recursion (Def 4.1 / eq 10), |
| iGCV (eq 11-12), fast structured samplers. |
| - `src/plotting.py` — Plotly + CSV export helpers. |
| - `claim{1,2,3,4}_*.py`, `general_cov.py` — per-claim reproductions (linear theory, CPU). |
| - `cifar_selfdistill.py` — PEP-723 UV script for the ResNet-50/CIFAR-10 experiment (HF GPU Job). |
| - `outputs/` — generated figures (HTML) + raw data (CSV) per claim. |
|
|
| ## Reproduce |
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|
| ```bash |
| # linear-theory claims (CPU, ~1 min each). WSL2 note: single-thread BLAS is faster. |
| uv run --env-file .env python claim1_decomposition.py # .env pins OPENBLAS_NUM_THREADS=1 |
| uv run --env-file .env python claim2_ushape.py |
| uv run --env-file .env python claim3_spectral.py |
| uv run --env-file .env python claim4_igcv.py |
| uv run --env-file .env python general_cov.py |
| |
| # deep-net claim (GPU): run on Hugging Face Jobs |
| hf jobs uv run --flavor a10g-small --secrets HF_TOKEN cifar_selfdistill.py \ |
| --n 10000 --K 4 --epochs 40 --etas 0.4,0.6,0.8 |
| ``` |
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|
