LLM Training Bottleneck: Horizon Length Causes Instability
A new empirical study on arXiv reveals that increasing task horizon length alone creates a training bottleneck for large language models (LLMs) used as interactive agents. The research systematically constructs controlled tasks where agents face identical decision rules and reasoning structures, differing only in the length of action sequences required for success. Results show that longer horizons induce severe training instability due to exploration difficulties and credit assignment challenges. The study identifies horizon reduction as a key principle to stabilize training. The paper is available at arXiv:2605.02572.
Key facts
- Study examines horizon length in LLM training for long-horizon tasks
- Controlled tasks isolate horizon length as the only variable
- Longer horizons cause training instability
- Instability driven by exploration difficulties and credit assignment challenges
- Horizon reduction is proposed as a key principle to address the bottleneck
- Paper published on arXiv with ID 2605.02572
- Focus on training dynamics rather than system or algorithmic improvements
- Agents face identical decision rules and reasoning structures across tasks
Entities
Institutions
- arXiv