New DeepSeek R1 is Really, Really Good Coder

DeepSeek R1 Upgrade Analysis

Key Concepts:

• DeepSeek R1 Upgrade: An improved version of the DeepSeek R1 model, excelling in coding and UI generation.
• Chain of Thought (CoT): A reasoning process where the model explains its thinking steps before providing an answer.
• Overthinking: The model spends excessive time and tokens on reasoning, even for simple queries.
• Live Codebench: A benchmark for evaluating coding capabilities of language models.
• Hugging Face: A platform for sharing and accessing machine learning models.
• Open Router: A US-based hosting service for language models.
• Reasoning vs. Non-Reasoning Model: The ability to enable or disable the chain of thought process.

Coding Prowess and UI Generation

• Significant Improvement: The upgraded R1 demonstrates a substantial improvement in coding capabilities compared to the original R1. The same prompt that failed with the previous generation now produces successful code.
• One-Shot Generation: Excels at generating code in a single attempt.
• UI Creation: Capable of creating impressive and interesting user interfaces, a feature not as prominent in the original R1. The model seems to be built on top of the new V3.
• Examples:
  • Falling Letters Animation: Successfully generates code for an animation with falling letters, gravity, and realistic collisions, a task the original R1 struggled with.
  • Bouncing Balls in Heptagon: Consistently generates accurate code for a complex animation involving 20 bouncing balls spinning within a heptagon, outperforming Gemini 1.5 Pro and matching Claude 3 Sonnet. Includes user controls for spin direction and speed.
  • Interactive Web Apps: Creates a functional view of the night sky with constellations, allowing rotation, stopping, and name hiding.
  • Cyberpunk Pokémon Website: Generates a futuristic-themed website featuring the first 25 legendary Pokémon with filtering and search capabilities.

Chain of Thought (CoT) Analysis

• Raw CoT Availability: DeepSeek provides raw chain of thought outputs, allowing users to understand the model's reasoning process. This is a valuable feature for learning and debugging, unlike summarized CoTs from other frontier labs.
• Organized Reasoning: The upgraded R1 exhibits a more organized and structured chain of thought compared to its predecessor. It considers multiple options before making a decision.
• Extended Thinking Time: The model can spend a significant amount of time (up to 10 minutes) on reasoning for complex prompts.
• Code Implementation in CoT: Similar to Gemini models, the upgraded R1 sometimes includes code implementation within the chain of thought.
• Example: In the trolley problem, the model correctly identifies that the victims are already dead, altering the ethical dilemma. It also references Philippa Foot, the creator of the trolley problem.

Reasoning Limitations and Overthinking

• Farmer Problem Flaw: The model fails to correctly solve a modified version of the farmer problem, indicating a reliance on training data rather than pure logical deduction. It attempts to transport all items (wolf, goat, cabbage) instead of just the goat as instructed.
• Overthinking Issue: The model tends to overthink even simple queries, consuming excessive tokens and time.
• Example: When asked for the capital of Pakistan, the model provides the correct answer but then unnecessarily delves into the city's history and location.
• Potential Solution: A pull request (PR) exists to convert the reasoning model into a non-reasoning model, similar to Gemini's feature for disabling thinking tokens. This would allow users to use the same model for both reasoning and non-reasoning tasks.

Benchmarks and Release Strategy

• Live Codebench Scores: The model performs impressively on the live codebench, rivaling GPT-4.
• Unofficial Release: DeepSeek has not officially released benchmarks, only the model weights on Hugging Face.
• Possible R2 Prototype: The upgraded R1 might have been intended as R2, but DeepSeek may be waiting for a more significant improvement before releasing a full R2 model.
• V3 Foundation: The upgrade seems to be based on the V3 model, which already showed coding improvements over the original V3.

Conclusion

The DeepSeek R1 upgrade represents a significant step forward in coding capabilities and UI generation. Its improved chain of thought and impressive performance on coding benchmarks make it a strong contender in the language model landscape. However, the overthinking issue and reliance on training data for certain reasoning tasks highlight areas for further development. The potential to disable the reasoning process through the proposed PR could make this model even more versatile. The speaker also offers to do a comparison between this model and Gemini 1.5 Pro or Claude 4 if there is interest.