On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While prior work has primarily focused on system-level optimizations or algorithmic improvements, the role of task horizon length in shaping training dynamics remains poorly understood. In this work, we present a systematic empirical study that examines horizon length through controlled task constructions. Specifically, we construct controlled tasks in which agents face identical decision rules and reasoning structures, but differ only in the length of action sequences required for successful completion. Our results reveal that increasing horizon l...
| Authors | Sunghwan Kim, Junhee Cho, Beong-woo Kwak et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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