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Main idea

Key point it to understand the below pictures

Iteration steps

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

Objective function

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• 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

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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

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?