UPenn paper routes weak models to strong ones via model disagreement, no labels needed
pappasg69 · x · 2026-10-21
A UPenn paper (Noorani, Kiyani, Pappas, Hassani) tackles weak-to-strong routing without correctness labels, which are hard to define and scale in open-ended/agentic tasks. They characterize the optimal routing policy over multiple locally-sampled weak-model responses, then approximate it with a model-disagreement signal requiring no ground truth. Across agentic and single-turn tasks it trades off cost vs. accuracy effectively, and can even beat methods trained to predict correctness when labels are available.
More from coding & agent
- Developer Moves AI Agents to a Dedicated Linux Workstation Using Incus System Containers — kevinkern · 2026-10-21
- Motion Launches a General Video Agent That Edits Footage and Mimics Any Reference Style — mhdfaran · 2026-10-21
- MCP Platform NitroStack Wins Product Hunt Product of the Day, Focuses on Retention — EstablishmentFun4373 · 2026-10-21
- CodeRabbit Ships 5,000 PRs a Week With a Tiny Team, Hires by Strategy-Game Hours — omojumiller · 2026-10-21
- LukeW maps the five UI problems every AI-native company faces with agents — LukeW · 2026-10-21
- Claude Code over ADB guts a bloatware-laden Android TV, freeing ~90% more RAM — edwinarbus · 2026-10-21