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Kevin's books are solid. Disappointing that this one does not cover Deepseek's Group Relative Policy Optimization (GRPO) algorithm, from last March. Is it the S

by esafak 2y ago

Kevin's books are solid. Disappointing that this one does not cover Deepseek's Group Relative Policy Optimization (GRPO) algorithm, from last March. Is it the SOTA in LLM training?

maxrmk 2y ago
There’s some disagreement over whether or not GRPO is the important part of deepseek or not. I’m personally in camp “it was the data and reward functions” and that GRPO wasn’t the key part, but others would disagree.
- Zacharias030 2y ago
  I believe that if the reader is familiar with PPO, they will immediately understand GRPO as well. I‘ve heard people say that GRPO giving a zero gradient in cases where neither the current sample nor the group scores give any reward is advantageous for optimization. It avoids killing your base model with low signal to noise updates, that can be a problem in PPO where the critic usually causes a non-zero gradient even for samples where one would rather be like „problem too hard for now, skip.“ I‘d be curious to hear you lay out your thoughts though!
  - maxrmk 2y ago
    I _think_ that's a factor of using rule based reward functions and not actually a feature of GRPO? The original formulation of GRPO from deepseek math uses a neural reward model that is trying to predict human rankings of responses, and in that configuration won't see 0 gradient updates. Flipping it around, if you swapped out the neural reward model in PPO with a reward function that can return zero, I thiiinnkkk it would be able to produce zero (or very low) gradient updates. I'll be the first to admit that I don't know enough about the space to say though. I'm still a beginner here.
armcat 2y ago
Kevin's books tend to be more foundational based on battle-tested techniques (I love his probabilistic ML book series, https://probml.github.io/pml-book/ https://probml.github.io/pml-book/). GRPO is a relatively new technique introduced by the DeepSeek team, and their seminal DeepSeekMath paper is actually a great resource. In short, it improves over PPO by not having to train a separate critic, thereby saving computational resources.

by esafak 2y ago

Kevin's books are solid. Disappointing that this one does not cover Deepseek's Group Relative Policy Optimization (GRPO) algorithm, from last March. Is it the SOTA in LLM training?

maxrmk 2y ago

There’s some disagreement over whether or not GRPO is the important part of deepseek or not. I’m personally in camp “it was the data and reward functions” and that GRPO wasn’t the key part, but others would disagree.

Zacharias030 2y ago
I believe that if the reader is familiar with PPO, they will immediately understand GRPO as well. I‘ve heard people say that GRPO giving a zero gradient in cases where neither the current sample nor the group scores give any reward is advantageous for optimization. It avoids killing your base model with low signal to noise updates, that can be a problem in PPO where the critic usually causes a non-zero gradient even for samples where one would rather be like „problem too hard for now, skip.“ I‘d be curious to hear you lay out your thoughts though!
- maxrmk 2y ago
  I _think_ that's a factor of using rule based reward functions and not actually a feature of GRPO? The original formulation of GRPO from deepseek math uses a neural reward model that is trying to predict human rankings of responses, and in that configuration won't see 0 gradient updates. Flipping it around, if you swapped out the neural reward model in PPO with a reward function that can return zero, I thiiinnkkk it would be able to produce zero (or very low) gradient updates. I'll be the first to admit that I don't know enough about the space to say though. I'm still a beginner here.

armcat 2y ago

Kevin's books tend to be more foundational based on battle-tested techniques (I love his probabilistic ML book series, https://probml.github.io/pml-book/ https://probml.github.io/pml-book/). GRPO is a relatively new technique introduced by the DeepSeek team, and their seminal DeepSeekMath paper is actually a great resource. In short, it improves over PPO by not having to train a separate critic, thereby saving computational resources.