Reinforcement Learning Evolution
The discussion highlights the evolution of reinforcement learning algorithms, particularly the transition from reinforce to proximal policy optimization (PPO). It emphasizes the simplicity and effectiveness of PPO in handling high variance gradients while noting the distinct challenges posed by language models in reinforcement learning from human feedback (RLHF). The need for new implementation strategies for RLHF is underscored, as traditional methods may not apply effectively to language tasks.In this clip
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Google ships it: Gemma open LLMs and Gemini backlash
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