This week
Suggest a nameObligation Marketing for AI Builders
The 7-Rung LadderThe 8-Type TaxonomyWrapper or WinnerThe Sample ColumnThe Signal
Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is one of the first credible attempts at automating LM improvement via reinforcement learning without prohibitive GPU costs. The surrogate feedback trick (using a frozen model to approximate what actual retraining would show) is clever enough to matter. For labs working on efficient model development workflows, this changes the math on whether model iteration can become genuinely algorithmic rather than handcrafted.
The insight is structural: harness evolution (prompting, tool-calling, step control) fixes process failures; weight training fixes content failures. For builders extending small models like Qwen 3.5-4B, this means you have a roadmap—run your agent, categorize failure modes, and you'll know whether to iterate on the prompt layer or retrain. The numbers are good: the harness alone took 4B from 0.16 to 0.30 on DeepPlanning, then training kept going from there.