"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Emergency Pod: Reinforcement Learning Works! Reflecting on Chinese Reasoning Models DeepSeek-R1 and Kimi k1.5

394 snips
Jan 25, 2025
Discover the incredible advancements in artificial general intelligence with the reveal of two Chinese reasoning models, DeepSeek-R1 and Kimmy k1.5. The discussion highlights innovative reinforcement learning techniques that improve reasoning skills and drive emergent behaviors. A detailed comparison showcases how these models are closing the gap between Chinese and Western AI capabilities. Aspects of global competition and economic implications provide a broader context, making this an enlightening exploration of the future of AI.
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INSIGHT

R1-0's AlphaZero Inspiration

  • DeepSeek's R1-0 uses pure reinforcement learning, unlike earlier models trained on human data.
  • It learns by getting rewarded for correct answers, similar to AlphaZero's self-play.
INSIGHT

R1's Binary Reward System

  • DeepSeek's R1 model uses a simple, binary reward system during training.
  • It receives a reward for correct answers and no reward for incorrect ones.
ANECDOTE

GPT Web's Sparse Reward Problem

  • OpenAI's GPT web project failed due to the sparse reward problem.
  • The model didn't achieve success and thus received no signal to learn from.
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