DeepSeek R1 Cloned for $30?! PhD Student STUNNING Discovery

What is the Aha Moment in Deep Learning?

Introduction to the Aha Moment

• A PhD student at UC Berkeley successfully reproduced the "aha moment" of deep learning on his own model for just $30, prompting a breakdown of this achievement.

Understanding the Aha Moment

• The "aha moment" occurs during training, where an intermediate version of the model learns to allocate more thinking time by re-evaluating its initial approach.

• This behavior showcases the model's growing reasoning abilities and highlights how reinforcement learning can lead to sophisticated outcomes.

Reinforcement Learning Layer

• The second layer discussed by Dario from Anthropic involves reinforcement learning that rewards correct or incorrect answers, enhancing model performance.

• Creative tasks pose challenges for defining rewards since they often lack definitive right or wrong answers.

Achievements of Ja Pan at UC Berkeley

• Ja Pan tweeted about reproducing deep seek R10 in a countdown game using reinforcement learning with a 3 billion parameter language model.

• The breakthrough involved developing self-verification and search abilities through reinforcement learning, leading to an internal monologue capability.

Importance of Reward Functions

• A well-defined reward function allows models to learn effectively; it indicates whether their answers are correct or incorrect.

• Open-ended questions complicate this process as they do not have definitive answers, unlike math or logic problems which do.

How Does Deep Seek Work?

Countdown Game Application

• The countdown game provides a structured environment where players combine numbers with arithmetic operations to reach a target number, allowing for clear reward signals.

Example of Internal Monologue

• An example illustrates how the assistant uses numbers (19, 36, 55, and 7) step-by-step to arrive at the correct answer (65), showcasing its internal thought process.

Availability and Sponsorship

• Deep seek R1 models are available on AWS's Amazon Bedrock platform. Amazon has been proactive in adopting multimodal AI strategies.

Future Implications of Reinforcement Learning

Recipe for Success with Deep Seek R1

• The successful application involves using a base language model with prompts and ground truth rewards applied through reinforcement learning in specific games like countdown.

Potential Developments in AI Models

Understanding the Evolution of Model Performance

The Process of Model Development

• The model began with nonsensical outputs but gradually developed effective strategies like revision and search without external guidance.

• A proposed solution involved self-verification and iterative revision, showcasing performance improvements over time through experimentation.

Importance of Base Model Quality

• Findings indicate that the quality of the base model is crucial; models with 1.5B parameters and above learned to search, self-verify, and revise solutions effectively.

• Models below 1.5B parameters showed limited performance improvement, highlighting a correlation between model size and capability.

Instruction Tuning Insights

• The instruct model learns faster but converges to similar performance levels as the base model; its outputs are more structured, negating the need for extra instruction tuning.

• Reinforcement learning algorithms used did not significantly impact outcomes, suggesting flexibility in approach.

Task Dependency in Learning Behavior

• Different tasks influenced how models learned; for example, multiplication required breaking down problems step-by-step rather than searching or verifying.

• This adaptability raises questions about future applications of smaller models tailored for specific tasks.

Open Source Impact on Research

• The open-source nature of research allows widespread tinkering and innovation following significant findings from deep learning papers.

• Issues such as EOS token generation in Open Llama were identified as areas needing improvement to enhance overall performance metrics.

Cost and Training Observations

• A $30 reference pertains to training a 3B parameter model using PO reinforcement learning over 10 hours on H100 hardware.

• Initial results indicated a reduction in output length during training before increasing chain-of-thought length for improved performance.

Conclusion on Training Costs