@no_stp_on_snek: fine-tuning field notes you can give a model better judgment without teaching it anything new. i didn't add knowledge o…
Summary
The author shares field notes showing that fine-tuning can improve a model's judgment by steering attention without adding new knowledge or weights, effectively changing its instincts rather than its IQ.
View Cached Full Text
Cached at: 07/01/26, 12:07 PM
fine-tuning field notes
you can give a model better judgment without teaching it anything new. i didn’t add knowledge or make it bigger. i just nudged how it weighs what it already knows, and that was enough to move judgment and restraint. the behavior i wanted wasn’t about being smarter, it was about what the model reaches for first. (under the hood: steering attention only, no new weights.) you’re changing its instincts, not its IQ.
Similar Articles
@no_stp_on_snek: fine-tuning field notes how a model is built decides what you're even allowed to change. some models you can adjust fre…
A thread sharing field notes on fine-tuning, explaining how model architecture (e.g., heavily-quantized or mixture-of-experts) restricts which parts can be adjusted, urging practitioners to check model accessibility before planning work.
@no_stp_on_snek: Fine Tuning Field Notes Article: short version: it was less about making it smarter and more about giving it a backbone…
A practitioner shares surprising findings from fine-tuning a small open model to be genuinely better in practical use, not just on benchmarks.
@no_stp_on_snek: fine-tuning field notes small behavior tweaks are a waterbed: push one spot down and another pops up. i trained a model…
A fine-tuning practitioner observes that adjusting one behavior in a model often causes unintended changes elsewhere, like a waterbed effect. Fixing pushback refusal silently broke strict formatting adherence, and subsequent fixes led to over-agreement or excessive refusal.
@no_stp_on_snek: what actually surprised me fine-tuning a small open model. note im failry new in this area so some of this may seem obv…
A developer shares surprising lessons from fine-tuning a small open model, including that base models often already max out on intended improvements, the real weakness is behavior (caving), and fine-tuning requires careful measurement and balancing.
@no_stp_on_snek: https://x.com/no_stp_on_snek/status/2074471505128305095
A fine-tuning practitioner recounts discovering that a small open model's weakness wasn't intelligence but a people-pleasing 'backbone' that caused it to cave under pressure, and how training to correct that inadvertently broke formatting ability, requiring a additive balancing approach rather than subtraction.