Implementation:Allenai Open instruct Layer Init
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement Learning from Human Feedback, Reward Modeling, Weight Initialization |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Concrete tool for initializing a neural network layer's weights with a normal distribution of specified standard deviation, provided by Open Instruct.
Description
The layer_init function is a lightweight utility that reinitializes the weight tensor of a given nn.Module layer using a normal distribution with mean 0 and a specified standard deviation. It performs in-place modification of the layer's .weight parameter using torch.nn.init.normal_.
In the context of reward model training, this function is called to initialize the score head (model.score) of an AutoModelForSequenceClassification model. The standard deviation is set to where is the model's hidden size, following the recommendation in Stiennon et al. (2020) (p. 11). This ensures that initial reward predictions are close to zero and have controlled variance.
The function is intentionally minimal: it only initializes the weight tensor and does not modify the bias. This is consistent with the typical HuggingFace AutoModelForSequenceClassification score head, which may or may not have a bias term depending on the underlying model architecture.
Usage
Use this function when you need to reinitialize the weights of a linear layer with a specific standard deviation. It is primarily used during reward model setup to initialize the score head before training begins.
Code Reference
Source Location
- Repository: Open Instruct
- File:
open_instruct/reward_modeling.py, lines 160-162
Signature
def layer_init(layer: nn.Module, std: float):
torch.nn.init.normal_(layer.weight, std=std)
return layer
Import
from open_instruct.reward_modeling import layer_init
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| layer | nn.Module | Yes | The neural network layer whose weights will be reinitialized. Must have a .weight attribute (e.g., nn.Linear). In practice, this is the model.score attribute of an AutoModelForSequenceClassification.
|
| std | float | Yes | The standard deviation of the normal distribution used for initialization. For reward model score heads, this is typically 1 / np.sqrt(model.config.hidden_size + 1).
|
Outputs
| Name | Type | Description |
|---|---|---|
| layer | nn.Module | The same layer passed as input, with its .weight parameter reinitialized in place. The layer is returned to support method chaining.
|
Usage Examples
Basic Usage
import numpy as np
import torch.nn as nn
from open_instruct.reward_modeling import layer_init
# Initialize a linear layer with small standard deviation
linear = nn.Linear(4096, 1)
layer_init(linear, std=1 / np.sqrt(4096 + 1))
# linear.weight is now drawn from N(0, 1/sqrt(4097))
As Used in Reward Model Setup
import numpy as np
from transformers import AutoModelForSequenceClassification
from open_instruct.reward_modeling import layer_init
model = AutoModelForSequenceClassification.from_pretrained(
"allenai/tulu-2-7b", num_labels=1
)
# Initialize score head: std = 1/sqrt(hidden_size + 1)
# For a 7B model with hidden_size=4096, std approx 0.0156
layer_init(
model.score,
std=1 / np.sqrt(model.config.hidden_size + 1)
)
Dependencies
| Package | Module | Purpose |
|---|---|---|
| torch | torch.nn | Provides nn.Module base class for the layer parameter
|
| torch | torch.nn.init | Provides normal_ for in-place normal initialization
|
Implementation Details
The function is intentionally simple and follows the principle of doing one thing well:
- It calls
torch.nn.init.normal_(layer.weight, std=std)which modifies the weight tensor in place, drawing new values from . - It returns the layer to allow optional chaining (e.g.,
model.score = layer_init(nn.Linear(d, 1), std=...)), though in practice it is called for its side effect. - The bias term (if present) is not reinitialized; it retains its default initialization (typically zeros).