Title: Extended Context, Enhanced Reasoning: Investigating How Long-Context Capabilities Drive Logical Processing

Abstract:

While recent language models have demonstrated robust reasoning skills, the specific impact of long-context capacity on these abilities has not been thoroughly investigated. This study posits that the current bottlenecks in model reasoning are partly attributable to inadequate long-context handling. This hypothesis is supported by empirical evidence, including the observation that larger context windows generally correlate with superior reasoning outcomes, and the similarity between patterns of reasoning failures and those seen in long-context processing tasks.

To validate this premise, we analyzed whether boosting a model’s long-context proficiency prior to Supervised Fine-Tuning (SFT) results in enhanced reasoning capabilities. Our methodology involved comparing models that shared the same architecture and fine-tuning datasets but possessed different levels of long-context capacity. The data uncovered a consistent pattern: models equipped with stronger long-context abilities achieved markedly higher accuracy on reasoning benchmarks following SFT. Importantly, these improvements remained evident even in tasks involving short inputs, suggesting that long-context training provides broad, generalizable advantages for reasoning.

These results indicate that modeling long contexts is not merely a tool for managing extensive inputs, but rather a fundamental prerequisite for effective reasoning. Consequently, we recommend that long-context capacity be prioritized as a primary objective in the architecture of future language models.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC