HINT-SD improves long-horizon LLM agent training by using hindsight to target self-distillation on failure-relevant action spans, delivering up to 18.8% higher performance and 2.26x lower time per step than dense per-turn feedback.
A pp W orld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agents
HINT-SD improves long-horizon LLM agent training by using hindsight to target self-distillation on failure-relevant action spans, delivering up to 18.8% higher performance and 2.26x lower time per step than dense per-turn feedback.