Comparing Novasky with S1:

NovaSky by Berkeley club, S1 by Feifei Li (arXiv:2501.19393), are the players who don't have capital or compute, mainly focuses on developing method that finetune a large language model with curated minimal reasoning datasets. Sky-T1 trained an entire model, with datasets from diverse domains, 17K training examples. S1 is focusing more on test-time scaling which is extending the reasoning time when needed through "wait" token repeatedly when it want to terminate the reasoning. But the datasets used to finetune is much smaller, which is only 1000 chosen questions with detailed reasoning traces. One method is more versatile, the other one is focused on math problem by increasing inference time for better performance.

s1:
- Inference-Time Enhancement:
  Concentrates on leveraging additional computation during inference (test-time scaling) to boost performance without necessarily changing the model’s underlying architecture.
Sky-T1:
- End-to-End Training:
  Focuses on training a model that already incorporates strong reasoning capabilities across domains. While it might use some techniques to boost inference, the primary innovation lies in a cost-effective and efficient training process that achieves robust performance out-of-the-box.

The players with strongest tech team, but in stealth mode who only do frontier experimentation.

These are the most terrifying players, such as SSI, Keen technology hiding everything behind.

Looking into Deepseek:

Deepseek: A true technical breakthrough has emerged in which reasoning ability is achieved entirely through reinforcement learning (RL) without any reliance on supervised finetuning. This approach is grounded in a novel mathematical framework—Group Relative Policy Optimization (GRPO)—that employs an objective function to ensure the learning process is stable and gradual rather than chaotic, preventing sudden changes in behavior at each step. For every question, the old policy generates a batch of answers, and rewards are assigned based on the relative improvement of the entire set of answers rather than an absolute right-or-wrong measure; the policy is then updated in favor of answers with a positive advantage. The training follows a simple template, typically formatted as a sequence of a “<think>” followed by an “<answer>,” and an “aha moment” emerges as the system develops self-refle...

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