Main idea

Key point it to understand the below pictures

Iteration steps

• for each input, generator G outputs
• for each output, calculate logits_prob for each token in current, old, reference model
• calcualte objective value as loss
• update old model in each step
• update reference model in each epoch

Objective function

• G is amount of outputs in each group for each input
• O_i is i-th output in current group
• t is index of tokens in O_i
• q is input
• O_i,t is t-tokens in i-th output
• pi is model parameter

KL value

Hyper parameters

Name in huggingface-trl

• beta weight for KL-value between current model and reference model, increase to avoid over-fitting
• num_iterations Numbers of iteration per batch, GRPO iterations times in Algorithm 1 picture, similar with LR
• epsilon for both clip lower_bound and upper_bound
• epsilon_high repalce epsilon for clip upper_bound when exist
• sync_ref_model bool, whether to Whether to synchronize the reference model with the active model every ref_model_sync_steps steps, using the ref_model_mixup_alpha parameter
• ref_model_mixup_alpha float, default 0.6, π_ref = α * π_θ + (1 - α) * π_ref_prev
• ref_model_sync_steps int, default 512, To use this parameter, you must set sync_ref_model=True.

FAQ

Q: How to cold start?

A: In first step, we know advantages for each output, which can push parameters updating to make objective value as much as possible

Q: How to simplify Zoom up/down in objective function?

This post is licensed under CC BY 4.0 by the author.