🚀 Introducing SkillOpt — an optimizer for agent skills.
Instead of finetuning model weights, we treat a natural-language skill as a trainable external parameter.
Think of it as deep learning for the frontier-model + agent era: learning rate, LR schedule, mini-batch, batch size, epoch, momentum — all in text-space optimization.
SkillOpt enables stable, controllable skill updates through bounded edits, allowing the optimizer to summarize “gradient directions” from agent experience and continuously improve procedural capability.
We evaluate SkillOpt across 6 benchmarks and 7 models, under both direct model calls and real agent execution loops with Codex + Claude Code. SkillOpt achieves best or tied-best results in 52/52 settings.
Train the skill, not the model. 🛠️🤖
🌐 https://t.co/zinqcX2wfQ
📄 https://t.co/pCI4VWdpih
BREAKING | @PublicisGroupe has agreed to acquire @LiveRamp for $2.2 billion in a deal funded by a mix of cash and debt.
Read more about the acquisition 👉 https://t.co/hxOJHCKRYa
@AdtechGod You’re absolutely right. Optimizing without any conversion signals allows the campaign the headroom to do its best work, and that’s a nuance most people miss.