DeepSeek R1 GAVE ITSELF a 200% Speed Boost - Self-Evolving LLM

Deep Seek R1: The Era of Self-Improving AI

Introduction to Deep Seek R1

• Deep Seek R1 has achieved a 2X improvement in speed through self-discovery, marking a significant milestone in the development of self-improving AI.

• We are approaching an intelligence explosion where AI can reach PhD-level intelligence and recursively improve itself.

Recent Discoveries and Cost Reductions

• A recent achievement by another team demonstrated the "aha moment" from the Deep Seek paper for just $3, showcasing a 10x reduction in cost compared to previous efforts.

• Simon Wilson's blog highlights that 99% of the code improvements were generated by Deep Seek R1 itself, emphasizing its autonomous capabilities.

Prompts and Iterative Improvements

• A user shared their experience with Deep Seek R1, detailing how they prompted the model to rewrite complex code effectively.

• The iterative process involved providing problem descriptions and past attempts, allowing the model to optimize existing code significantly.

Examples of Code Optimization

• One prompt tasked Deep Seek R1 with converting C++ ARM Neon SD to WASM SIMD, demonstrating its ability to enhance parallel processing efficiency.

• Another example showed how the model could implement similar patterns from existing code, illustrating its logical reasoning capabilities.

Implications for Future AI Development

• The potential for numerous agents running autonomously suggests we are nearing a critical point in AI development that could lead to superintelligence.

• Experts like Yan LeCun argue that AGI emergence will be gradual rather than instantaneous, highlighting differing perspectives on AI progression.

Open Source vs. Closed Source Innovations

• The importance of open-source models like Deep Seek R1 is emphasized as they accelerate innovation compared to closed-source counterparts.

Introduction to R1V and Reinforcement Learning

Overview of R1V

• The R1V project utilizes reinforcement learning with verifiable rewards, similar to techniques used in Berkeley PhD and DeepCar projects.

• This method is effective when there is a well-defined reward function, allowing for clear input-output relationships typical in STEM fields.

Emergent Behavior and Model Efficiency

• The approach enables models to develop emergent thinking behaviors, even with small-scale models costing only a few dollars.

• A 2 billion parameter model demonstrated superior performance over a 72 billion parameter model after just 100 training steps at minimal cost.

Future Directions in AI Models

• The trend may shift towards numerous small models with core intelligence, each trained on specific tasks using reinforcement learning.

• There’s potential for a routing model that selects the appropriate smaller model based on user prompts, moving away from reliance on large generalized models.

Performance Results

• After 100 training steps, the 2 billion parameter model improved from 53% accuracy to nearly perfect (99%), outperforming the larger model's 94%.

Vision for R1V Framework