Agentic AI news, research notes, and industry analysis.
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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 →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 post-training method called Frontier Learning dynamically generates reasoning problems calibrated to a model's current capability, using a regret signal to stay at the edge of what the model can reliably solve. On a probability reasoning task it achieves a 115% relative gain over fixed-pool baselines.
Read more →DeepSeek's DSec paper describes the infrastructure behind their agentic training: 160 nodes, 380k concurrent sandboxes, 5,000+ creations per second, roughly 3 million environments per day. The architecture choices — layered images, memory sharing, typed sandbox registry, separation of stateful rollouts from preemptible GPU jobs — solve a problem most labs are only beginning to hit.
Read more →A new paper and blog post from Michael Noukhovitch identifies the "Matthew Effect" in RL training for LLMs: standard GRPO concentrates gains on problems a model can already mostly solve, while genuinely hard problems see minimal improvement. The fix — Never Give Up (NGU) — is an adaptive sampling strategy that keeps generating completions until it finds one that works, naturally redistributing compute toward the problems that need it.
Read more →A researcher prefilled Qwen3.8 A95B with the first 1% of a GPT-5.5 Pro chain-of-thought and measured how much the model continued following it. The resulting +18 pp jump in overlap — much larger than seen in other models — is a practical signal that Qwen's post-training data may include GPT-5.5 Pro reasoning traces, and points to a general method for probing where a model learned to think.
Read more →Hugo Vergnes trained a 3.8B-parameter LLM for $998 in compute costs, scoring 0.384 on the CORE benchmark. The interesting result isn't the price — it's that doubling context length from 1024 to 2048 tokens drove 83% of the score improvement on one key task, which says something about what benchmark numbers are actually measuring.
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.
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