SPIN Unprocessed July 8, 2026 ai_technology research
TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
View original on arxiv.orgOverview
arXiv:2607.05804v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. However, its application to long-horizon agentic tasks remains insufficiently explored. We identify two key inefficiencies in vanilla agent OPD: (1) full-horizon rollouts often waste wall-clock resources on tail turns that provide weak and noisy KL supervision, and (2) tra
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Artificial Intelligence
View all →- SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation
- PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
- Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
- StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
- Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
- From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO