Qwen Paper Proposes Elastic Horizon, a Closed-Loop Controller for Agentic RL Interaction Budgets
dair_ai · x · 2026-09-11
A Qwen team paper tackles interaction horizon scheduling in agentic RL: scaling per-episode environment interactions helps long-horizon agents, but existing curricula are open-loop and monotonic.
The authors define the "effective interaction frontier" — beyond it, extra interactions yield diminishing returns while costs grow linearly, with clear saturation plateaus on AppWorld and BFCL. Elastic Horizon is a closed-loop controller that tracks this boundary using the 90th percentile of successful trajectory lengths, a statistic already available during training.
More from coding & agent
- Claude Code finds root backdoor in Honda Civic head unit, then debloats it — IndraVahan · 2026-10-26
- Claude Code + Monid MCP builds a $12K/year B2B prospecting engine on live data — aryanXmahajan · 2026-10-26
- Karpathy shares CLAUDE.md rulebook to stop AI coding agents from piling up tech debt — mdancho84 · 2026-10-26
- Rethinking dependencies in the agent era: drop libs that save 20 lines of code — gethackteam · 2026-10-26
- RustGIS hits 2,000 GitHub stars in two weeks with agent-first architecture — giswqs · 2026-10-26
- Vercel Ships 12 New Products at Its 2026 Conference as It Races Rivals for the Agent Entry Point — FinanceYF5 · 2026-10-26