Principle:Huggingface Trl Reward Argument Configuration
| Property | Value |
|---|---|
| Principle Name | Reward Argument Configuration |
| Technology | Huggingface TRL |
| Category | Configuration |
| Workflow | Reward Model Training |
| Implementation | Implementation:Huggingface_Trl_TrlParser_RewardConfig |
Overview
Description
Reward model training in Huggingface TRL requires a specialized configuration class that extends the standard Transformers TrainingArguments with parameters specific to preference-based reward modeling. The RewardConfig dataclass encapsulates all hyperparameters needed to train a reward model using the Bradley-Terry framework, including learning rate defaults tuned for reward tasks, sequence length constraints, reward centering regularization, and dropout control.
The configuration is parsed at runtime through the TrlParser utility, which extends Huggingface's HfArgumentParser with support for YAML config files and environment variable management. This design allows reward training to be launched from the command line or configured programmatically.
Usage
RewardConfig is used as the args parameter when instantiating a RewardTrainer. It can be created directly in Python or parsed from command-line arguments and YAML configuration files using TrlParser.
Theoretical Basis
Bradley-Terry Reward Modeling Hyperparameters
The Bradley-Terry model defines the probability that a human prefers response A over response B as:
P(A > B) = sigmoid(r(A) - r(B))
where r is the scalar reward function. The training process learns this reward function from pairwise human preferences. Key hyperparameters that control this process include:
- learning_rate (default: 1e-4): A lower learning rate than standard fine-tuning (which typically uses 5e-5) is recommended for reward model training to prevent the classification head from overfitting to preference patterns before the backbone adapts.
- max_length (default: 1024): Controls the maximum tokenized sequence length. Both chosen and rejected responses are filtered if they exceed this value. Setting this appropriately prevents memory issues while ensuring sufficient context for meaningful preference judgments.
- gradient_checkpointing (default: True): Enabled by default for reward training since sequence classification over long contexts is memory-intensive.
Center Rewards Regularization
The center_rewards_coefficient parameter implements a regularization technique proposed by Eisenstein et al. (2023) that incentivizes the reward model to output mean-zero rewards. The regularization loss term is:
L_center = coefficient * mean((r_chosen + r_rejected)^2)
This prevents reward hacking where the model learns to assign uniformly high or low scores. A recommended value of 0.01 provides mild regularization without significantly affecting the primary preference loss.
Dropout Disabling
The disable_dropout parameter (default: True) turns off all dropout layers in the model during reward training. This is standard practice for reward model training because:
- Reward models need deterministic outputs for the same input to provide consistent training signals in downstream RLHF.
- The pairwise comparison structure of the loss already provides implicit regularization, reducing the need for dropout.
- Dropout introduces noise in reward predictions that can destabilize PPO training when the reward model is used as a scoring function.