Topic
Deepseek R1
4 episodes
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Overview: Supervised Fine Tuning
We finally slow down and make supervised fine-tuning click, because we keep leaning on S F T like everyone already has the whole shape of it. We build it from the apprentice-and-worked-examples picture into the actual training loop, the examples, and the trade-offs.
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LongTraceRL: Learning Long Context Reasoning from Search Agent Trajectories with Rubric Rewards
Justy and Cody unpack LongTraceRL, a paper that trains long-context reasoning models using realistic search-agent distractors and entity-level rubric rewards, with a short look at what would make it shippable.
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Many Shot CoT ICL: Making In Context Learning Truly Learn
Justy and Cody dig into a paper arguing that long-context chain-of-thought prompting behaves less like stuffing a prompt with relevant examples and more like teaching the model during inference. They unpack why many-shot tricks from classification break on reasoning, why semantic retrieval stops helping, and how the paper’s Curvilinear Demonstration Selection tries to order examples like a smooth mini-curriculum.
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How to Fine Tune a Reasoning Model? A Teacher–Student Cooperation Framework to Synthesize Student Consistent SFT Data
Episode 300 of Exploring Next digs into TESSY, a teacher-student data synthesis method for fine-tuning reasoning models without wrecking the smaller model’s existing style. The hosts unpack why direct teacher-generated supervised fine-tuning can actually make reasoning models worse, how TESSY alternates teacher-generated capability tokens with student-generated style tokens, and why that matters for anyone trying to ship smaller, cheaper reasoning systems for coding and other structured tasks.