Reinforcement learning (RL) is becoming a vital tool for improving chain-of-thought in reasoning models. Recent models like DeepSeek-R1 and Kimi k1.5 have used RL to refine their reasoning steps, generating more accurate solutions for complex domains such as math, coding, and science. Unlike other training methods, RL rewards models for generating better sequences, allowing them to self-improve. Learn more in The Batch: hubs.la/Q0351_T10
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@DeepLearningAI The reasoning steps make them vulnerable to jailbreaks
@DeepLearningAI The reasoning steps make them vulnerable to jailbreaks
@DeepLearningAI RLs impact on reasoning is still evolving.
@DeepLearningAI rl's impact on reasoning is interesting, but data matters.
@DeepLearningAI RL's role in enhancing reasoning models like DeepSeek-R1 and Kimi k1.5 is fascinating. It's like refining the mind's pathways to solve complex problems more efficiently, akin to a tech alchemist's quest for perfection in thought processes.
@DeepLearningAI RL is the secret sauce for sharper AI brains.
@DeepLearningAI Sounds like machines are levelling up their thinking game! Wonder if they'll ever beat us at guessing where lost socks go.
@DeepLearningAI reinforcement learning opens a treasure chest for model inspiration!
@DeepLearningAI using rl for reasoning is like giving a brain a personal trainer!
Reinforcement learning is redefining how AI thinks—not just what it knows. By rewarding better reasoning sequences, models evolve beyond static training data, refining logic in real-time. As RL-driven approaches like DeepSeek-R1 and Kimi k1.5 advance, we’re witnessing AI move closer to true problem-solving intelligence. Exciting times for AI reasoning!
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@DeepLearningAI Exciting to see how reinforcement learning is enhancing reasoning in models like DeepSeek-R1 and Kimi k1.5!