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9 stories in this tag.
Figma's October 29 writeup describes how it trained its own canvas-editing model using synthetic SFT data followed by RL with designer-calibrated reward models. The results — 20% faster tasks, 60% fewer undos — are more interesting than the benchmark numbers, because undo rate is the one metric that tells you whether a model actually understood what a user meant.
Read more →WorldTUI is a 360M-parameter model fine-tuned to predict terminal screen changes from keystrokes, with no shell running behind it. It scores 80/106 on ShellBench and runs in-browser at int4 via WebGPU. As a demo of narrow world modeling it's impressive; as a reliable terminal it isn't — but that gap is the interesting part.
Read more →Sakana AI and UC Berkeley's MASS paper frames recursive self-improvement not as a single model thinking harder, but as a cycle of evolutionary workflow search, self-evaluation, and LoRA fine-tuning on the model's own best multi-agent traces — with the workflow text deliberately withheld during training.
Read more →A new paper characterizes sustained fine-tuning of a 3B LLM on an iPhone 17 Pro: one battery charge is enough for a typical personalization run, and a broken MLX kernel they found and fixed cuts energy use by a third and speeds training 1.47x.
Read more →Jeff is a set of Jev-compatible decision models — Qwen3.5 and Gemma 4 fine-tunes — trained from scratch by a single developer in 2–3.5 hours on one RTX PRO 6000. The 2B variant hits 83.1% on five public benchmarks where Jev scores 83.0%, closing the quality gap that made every prior open replica a workaround rather than a replacement.
Read more →Rohan Bansal trained a 4B parameter model to generate Postgres join-order hints via distillation from a frontier model followed by reinforcement learning with a custom anchored GRPO variant — yielding a 1.81× geometric mean speedup on 113 join-heavy queries for about $1,200 in total compute. The methodology shows how cheap specialist RL is getting for niche performance optimization tasks.
Read more →Fermi Sense and Ramp fine-tuned a 9B open-source model with GRPO for $500 and outperformed every frontier configuration on a catalog review task — 87.3% vs 76.9%, at 40x lower inference cost. The benchmark has a Goodhart's Law concern, but the underlying economics of task-specific RL fine-tuning are real and worth taking seriously.
Read more →A new paper finds that reinforcement learning gains in transformers concentrate almost entirely in a narrow band of middle layers. Training just one layer at roughly 40–60% network depth can match or exceed full-parameter RL fine-tuning. The finding challenges the assumption that all layers participate equally in post-training, and has practical implications for compute-efficient alignment.
Read more →Alibaba's Qwen team released Qwen-Scope, sparse autoencoder weights for Qwen3 and Qwen3.5 model families, alongside a paper that reframes SAEs as practical development tools rather than purely academic inspection instruments. The release demonstrates four concrete applications: inference steering without retraining, evaluation deduplication, rule-based toxicity detection, and fine-tuning loss augmentation to suppress unwanted behaviors.
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