This change initializes the reward_kwargs variable as an empty dictionary to avoid potential UnboundLocalError during its usage in the GRPOTrainer class. This ensures that the variable is defined before it is accessed.

What does this PR do?

This PR addresses a potential UnboundLocalError that can occur in trl.trainer.GRPOTrainer._generate_and_score_completions.

The reward_kwargs dictionary is intended to store additional arguments (beyond prompt and completion) that are passed to custom, non-module-based reward functions. This dictionary is populated within an else block when iterating through self.reward_funcs.

If self.reward_funcs is empty (e.g., when a downstream library or user provides an empty list, intending to bypass TRL's internal reward calculation and supply pre-computed rewards/advantages) or if all provided reward functions are instances of torch.nn.Module, the else block that defines reward_kwargs is never entered.

Later in the same method, there's a warning mechanism that checks if all reward functions returned None for a sample. This warning message attempts to access reward_kwargs to provide context:
row_reward_kwargs = {key: value[nan_row_idx] for key, value in reward_kwargs.items()}.
If reward_kwargs was not initialized due to the conditions mentioned above, this access results in an UnboundLocalError, crashing the process.

This PR fixes the error by ensuring reward_kwargs is always defined by initializing it to an empty dictionary ({}) before the loop that processes reward functions. This allows the warning logic to execute safely, even if no custom reward functions populate reward_kwargs.