Following up on my reasoning model article, I just read the new "s1: Simple Test-Time Scaling" paper, which describes an interesting method for improving reasoning models using a combination of pure supervised finetuning (SFT) and scaling inference compute.
In short, their
@rasbt
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S1 Simple Test-Time Scaling: Improving Reasoning Models with SFT
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Reasoning Models vs General LLMs Post-Training Techniques
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Thanks for clarifying. E.g. in my view, "Pre-training, post-training, post-inference," would be too general because that's also the same flow for LLMs that are not specialized reasoning models. In a sense all reasoning techniques are post-training techniques because they all are
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Intermediate model SFT data reuse for distillation
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The way I read the paper, it was the intermediate model that generated the SFT data, and then they just reused that for distillation. But pls correct me if there some counterpoint to that in the paper.
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Clarification on inference-time scaling versus test-time scaling terminology
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Thanks for the comment! Actually, this part confused me a bit:
"whereas the methods you listed are ways we can induce test-time scaling – and so can't be meaningfully compared to "inference-time scaling"."
That's because "inference-time scaling" and "test-time scaling" should be -
Next Generation Models for Tabular Data Problems
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Big congrats, Frank, that's very exciting. Actually, I believe a huge portion of real-world problems are tabular data problems. And I feel like we were stuck there with XGBoost for a decade, it's exciting to finally see a next gen of models for tabular data problems!
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Reasoning Models Limited by Lack of Multimodal and Function-Calling Abilities
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That's actually a very good and interesting point. I think the challenge is currently that those reasoning models don't have multimodal and function-calling abilities yet, so while they might be useful for EDA from a reasoning perspective, the usage would be quite clumsy there.
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Distilled Models: 32B vs 3B Trade-offs and Use Cases
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Agree with you. Do you mean the 32B distilled models or the 3B one I hinted at at the end? I think 32B should be just fine but yeah, 3B will be more tricky; I'd say that one is more for educational purposes.
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Understanding Reasoning LLMs: DeepSeek R1 and Building Methods
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I just finished writing up my take on reasoning models: https://
magazine.sebastianraschka.com/p/understandin
g-reasoning-llms
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Here, I
1. Discuss the advantages &d disadvantages of reasoning models
2. Of course, describe and discuss DeepSeek R1
3. Describe the 4 main ways to building & improving reasoning models -
LLM Distillation Term Usage: R1 Dataset Curation and Model Training
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Today, in LLM contexts the term "distillation" is used quite loosely. In the case of R1 it just means that they created and curated a dataset for SFT from R1 that they used to train distilled R1 models based on Qwen and Llama.
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Questioning the necessity of LLM-based operating systems
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Except, I don't know if we want/need an LLM-based OS . Reminds me a bit of the web 3.0 on blockchain topic