PopRiskMinimization

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PopRiskMinimization

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Operationalization of Population Risk Minimization algorithm from "A Theory of Generalization in Deep Learning."

PopRiskMinimization 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Population Risk Minimization for Neural Networks

`popriskmin` gives you `PRM`, a small modification of AdamW that trains on population risk instead of raw empirical risk. It keeps AdamW's usual one forward pass and one backward pass per step, then preconditions each parameter update with the population-risk mask from Litman & Guo (2026), A Theory of Generalization in Deep Learning.

PRM tracks one extra tensor per parameter tensor: an exponential moving average of centered minibatch gradient variance. For each scalar parameter $k$, it asks whether the batch-mean gradient is larger than the leave-one-out noise estimate:

$$
\mu_k^2 > \alpha \sigma_k^2
$$

Parameters that pass get the Adam update. Parameters that fail get shrunk or zeroed, depending on the mask. On the fresh-batch boundary, $\alpha = 1$ and the streaming variance estimates the leave-one-out penalty $\Sigma_B / (b - 1)$.

## Install

```sh
uv pip install -e .
uv sync --extra test
```

`PRM` requires `torch>=2.0`.

## Quick start

```python
from popriskmin import PRM

optimizer = PRM(
    model.parameters(),
    lr=3e-4,
    weight_decay=0.01,
    softness=1.0,
    batch_size=32,
)

for batch in loader:
    optimizer.zero_grad()
    loss = loss_fn(model(batch.x), batch.y)
    loss.backward()
    optimizer.step()
```

Useful options:

```python
PRM(model.parameters(), mask="snr")         # default, smooth SNR mask
PRM(model.parameters(), mask="soft")        # strict Algorithm 1 cutoff
PRM(model.parameters(), mask="hard")        # 0/1 theorem mask
PRM(model.parameters(), reduction="per_tensor")
```

Use `reduction="per_tensor"` when each scalar parameter is noisy but the whole parameter tensor has a clear signal. This is often the more useful setting for large generative models, diffusion, and CFM-style training.

## Masks

| `mask` | Formula | Behavior |
|--------|---------|----------|
| `snr` | $\hat{m}^2 / (\hat{m}^2 + \lambda_p \alpha \hat{s} + \varepsilon)$ | Default. Smooth, never fully shuts off. With `softness=1`, it gives $q = 1/2$ on the boundary. |
| `soft` | $\max(\hat{m}^2 - \alpha \hat{s}, 0) / (\max(\hat{m}^2 - \alpha \hat{s}, 0) + \lambda_p \hat{s} + \varepsilon)$ | Strict Algorithm 1 mask. Zero below the boundary. |
| `hard` | $\mathbf{1}[\hat{m}^2 > \alpha \hat{s}]$ | Binary indicator from Theorem 6.5. Mostly useful for ablations. |

`softness` is $\lambda_p$. Larger values make the mask more conservative. A reasonable first sweep is `0.3, 1, 3, 10`.

## Arguments

| Argument | Default | Notes |
|----------|---------|-------|
| `lr` | `1e-3` | Tune as you would for AdamW. |
| `betas` | `(0.9, 0.999)` | Adam moment decay rates. |
| `rho` | `0.99` | Decay for the centered gradient variance. Usually shorter than `beta2`. |
| `eps` | `1e-8` | Stabilizer for Adam and the mask denominator. |
| `weight_decay` | `0.01` | Decoupled AdamW-style weight decay by default. |
| `softness` | `1.0` | Population-risk mask regularizer. |
| `batch_size` | `None` | Optional for `boundary="batch"`. Required for `boundary="empirical"`. |
| `boundary` | `"batch"` | Use `"batch"` for online or fresh-batch training. Use `"empirical"` for finite-dataset leave-one-out. |
| `n_dataset` | `None` | Required when `boundary="empirical"`. |
| `mask` | `"snr"` | One of `"snr"`, `"soft"`, or `"hard"`. |
| `reduction` | `"per_param"` | Use one mask per scalar parameter. Set `"per_tensor"` to pool each parameter tensor first. |
| `warmup_steps` | `0` | Force the mask to 1 for the first N optimizer steps. |
| `amsgrad` | `False` | Use AMSGrad for the Adam denominator. |
| `bias_correction` | `True` | Apply Adam-style bias correction to `m`, `v`, and `s`. |
| `decoupled_weight_decay` | `True` | Set to `False` for coupled L2. |

## Diagnostics

```python
stats = optimizer.get_mask_stats()
print(stats)
```

Example output:

```python
{
    "mean_q": 0.62,
    "active_fraction": 0.71,
    "min_q": 0.00,
    "max_q": 0.99,
    "parameter_count": 1_245_184,
    "noise_scale": 12.4,
    "signal_sq": 18.7,
    "snr": 1.51,
}
```

`mean_q` tells you how open the mask is on average. If `active_fraction` falls
near zero, almost every parameter is below the leave-one-out boundary. Try a lower `softness`, a higher learning rate, or `reduction="per_tensor"` before assuming the method is broken.

## When it helps

PRM is meant for settings where plain empirical-risk training fits structured noise or memorizes before it generalizes. The paper reports improvements in:

| Setting | AdamW | PRM |
|---------|-------|-----|
| Modular division, 25% train fraction | Groks at step 29,450 | Groks at step 5,950 |
| PINN with noisy initial condition, $\beta = 5$ | Best LR-tuned run: 3,300 iterations | 1,400 iterations to relative $\ell_2 \le 0.40$ |
| Qwen2.5-0.5B-Instruct with 30% noisy DPO | Reward accuracy 0.566, drift 0.41 | Reward accuracy 0.641, drift 0.14 |

With more experiments in the appendix. However, PRM is not magic. If AdamW already reaches the solution without overfitting, PRM may not help.

## Example

Run the smoke test:

```sh
uv run python examples/synthetic_regression.py
```

The script trains AdamW and several PRM variants on a noisy regression problem. You should see the optimizer learning the underlying signal instead of the corrupted training labels.

## Layout

## Citation

```bibtex
@misc{litman2026theory,
  title         = {A Theory of Generalization in Deep Learning},
  author        = {Litman, Elon and Guo, Gabe},
  year          = {2026},
  eprint        = {2605.01172},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  doi           = {10.48550/arXiv.2605.01172}
}
```

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