TEMPO evaluates progress in long-horizon tasks
thetripathi58 · x · 2026-08-26
Long-horizon tasks are hard because agents can spend hours acting without knowing if they are closer to the goal. TEMPO allows the model to switch between actor and critic at macro-steps to evaluate progress. For instance, in a knight-placement task, the Critic distinguished between trajectories with the same reward but different directions.
More from coding & agent
- Knowledge Compressor cuts doc tokens in half to save Agent context costs — mariorod1 · 2026-08-26
- OpenAI launches WebMCP Challenge for MCP-based web apps — kuanhoong · 2026-08-26
- OpenAI docs integrate WebMCP protocol — ColleenMBrady · 2026-08-26
- GLM-5.3-Flash Matches Claude Opus 4.8 on Code Bench — Zai_org · 2026-08-26
- Turn AI Investigations into Reusable PowerShell Commands — dfinke · 2026-08-26
- AI 3D workflow: generate parts with Tripo, let an agent assemble, rig and animate in Blender via MCP — majidmanzarpour · 2026-08-26