TalkRL: The Reinforcement Learning Podcast Arash Ahmadian on Rethinking RLHF
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Mar 25, 2024 Arash Ahmadian discusses preference training in language models, exploring methods like PPO. The podcast dives into reinforced leave one out method, reinforced vs vanilla policy gradient in deep RL, and token-level actions. Reward structures and optimization techniques in RLHF are also explored, emphasizing the importance of curated reward signals.
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RLHF Optimization Simplicity
- Optimizing LLMs with reinforcement learning is simpler than in deep RL.
- Reinforce and vanilla policy gradients are surprisingly effective.
RLHF Method Categories
- RLHF methods differ in online vs. offline approaches and reward model usage.
- DPO, IPO, and KTO are offline, skipping reward model training, while PPO and Reinforce are online.
Reinforce vs. PPO in RLHF
- PPO's complexity is unnecessary for RLHF with pre-trained LLMs.
- Reinforce is a better fit, and RLO offers a robust alternative to iterative fine-tuning like Raft.