Uploaded January 2026 | Updated September 2026, 1 week ago

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This video breaks down what's wrong with scaling RL for LLMs, especially in the direction of reaching AGI, but why RL still matters. As RL is noisy and can hurt generalization, yet it enables exploration and self-correction that pretraining can’t, we are stuck between a rock and a hard place with this direction. We’ll also look at why LoRA is becoming the practical way to do RL cheaply, swappable adapters that can match full fine-tuning on reasoning and make personalized agents easier to deploy, which might look like a promising future direction to apply RL on a massive scale.


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Dwarkesh Podcast w/ AK
[YouTube] youtu.be/lXUZvyajciY

Dwarkesh Podcast w/ Ilya
[YouTube] youtu.be/aR20FWCCjAs

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
[Paper] arxiv.org/abs/2506.01939

The Path Not Taken: RLVR Provably Learns Off the Principals
[Paper] arxiv.org/abs/2511.08567

LoRA Without Regret
[Blog] thinkingmachines.ai/blog/lora

Tina: Tiny Reasoning Models via LoRA
[Paper] arxiv.org/abs/2504.15777

Tinker
[Website] thinkingmachines.ai/tinker


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