DeepSeek R1 Cloned for $30?! PhD Student STUNNING Discovery
AI Summary
Summary of the Video Transcript
- A UC Berkeley PhD student, Ja Pan, reproduced the “aha moment” from the DeepSRL R10 model using reinforcement learning (RL) for under $30.
- The “aha moment” refers to a model’s emergent ability to allocate more thinking time to a problem by re-evaluating its initial approach.
- This behavior demonstrates the model’s growing reasoning abilities and the potential of reinforcement learning to produce sophisticated outcomes.
- The student applied RL to the countdown game, where the model developed self-verification and search abilities autonomously.
- The model’s performance improved with a well-defined reward function, which is easier to establish for tasks with definitive answers like math or logic.
- The experiment showed that the base model quality is crucial, with larger models (1.5B parameters and up) developing the ability to search and self-verify.
- The instruct model learns faster but converges to about the same performance as the base model, and its outputs are more structured and readable.
- The specific RL algorithm used (PPO, GRPO, or Prime) did not significantly affect the outcome.
- The model’s reasoning behavior is task-dependent, with different strategies emerging for different tasks.
- The findings are currently only validated on the countdown task and not general reasoning due to computational constraints.
- The student’s work is open-sourced under the name “tiny zero,” with all resources available on GitHub.
Detailed Instructions and URLs
- No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.