Rethinking Environments for Long-Horizon Work — Rayan Garg, Theta Software
Rayan Garg from Theta Software discusses how to measure and build environments for AI agents tackling long-horizon tasks.
The challenge is that there's no clear definition of what "long-horizon" means — many use time limits where agents reach a success threshold, but this is both noisy and misleading since the same duration can mask vastly different difficulty levels. How you choose to measure significantly impacts conclusions about the model. Garg's focus is on designing environments and verification systems that make these measurements honest by understanding how poor early decisions create cascading effects throughout a task, and by verifying results from final state outcomes rather than a judge's guess.
Vibekollen prepared this summary with AI from the original publication. The content belongs to AI Engineer.
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