Recent discussions in the field of offline reinforcement learning (RL) challenge the conventional belief that value function learning is the main bottleneck for scaling. New research suggests that policy learning, rather than value functions, may be the key bottleneck in offline RL scaling, contrary to previous assumptions.
Exploring Offline Reinforcement Learning (RL): Offering Practical Advice for Domain-Specific Practitioners and Future Algorithm Development #AI #AItechnology #artificialintelligence #llm #machinelearning #ReinforcementLearning https://t.co/2q4U4yqqsJ https://t.co/35pP6B1zbG
Exploring Offline Reinforcement Learning RL: Offering Practical Advice for Domain-Specific Practitioners and Future Algorithm Development https://t.co/HRRqTKKDwu #OfflineRL #AIresearch #DataDriven #RLchallenges #FutureAI #ai #news #llm #ml #research #ainews #innovation #artif… https://t.co/f8pEcm5yzy
How Reinforcement Learning from #AI Feedback works https://t.co/HTDZmOjCow @r_o_connor @AssemblyAI #DataScience #MachineLearning Cc @DeepLearn007 @terence_mills @KirkDBorne @FrRonconi @enilev @Khulood_Almani @bamitav https://t.co/I84QABk4ob
Most works in offline RL focus on learning better value functions. So value learning is the main bottleneck in offline RL... right? In our new paper, we show that this is *not* the case in general! Paper: https://t.co/1lsLPxrdR9 Blog post: https://t.co/BYXKEb49hO A thread ↓ https://t.co/XYA0zeteoJ
Conventional wisdom: the BIG blocker holding offline RL behind imitation / SFT, preventing good scaling, etc is the value function. But can we still do well with current value functions? We find: often *policy* learning bottlenecks offline RL scaling: https://t.co/3VPcgoBu1f 🧵 https://t.co/1kg0tnH3op