fine-tuning

Definition

Taking a model that already knows a lot and giving it more lessons so it gets better at one specific task.

Continuing the training of a pretrained model on a smaller task-specific dataset, updating its weights to adapt the general model to a particular use case.

Also called: Fine-tuning, fine-tune, fine-tuned, finetuning, finetune, finetuned, finetunes

Mentioned in 19 episodes

1. 078
   Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
2. 074
   How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
3. 071
   When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
4. 061
   When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
5. 054
   When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
6. 052
   An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
7. 048
   How a 30B Open Model Reached Olympiad Gold With the Right Recipe
8. 043
   When 'This Is False' Doesn't Stick: Why Models Learn the Lie Anyway
9. 040
   Two Frozen Models Learn to Whisper: Coupling Through Hidden States
10. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
11. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
12. 026
    What RL Actually Does to Language Models, at the Token Level
13. 022
    Training the Model Spec Directly: An Alignment Lever Aimed at the Say-Do Gap
14. 021
    Ten Thousand Examples Beat the Full Industrial Pipeline for Search Agents
15. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
16. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
17. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
18. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
19. 004
    The Sycophancy Circuit That Survives Alignment Training

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