Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge
Quick summary
arXiv:2608.12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories. This scaling reflects the implicit assumption that training on longer contexts will only help the model by exposing it to richer evidence. We challenge this view by studying how the context window shapes a model's mode of learning, shifting it between parametric internalization and contextualization. We propose the Information Abundance Paradox, which hypothesizes that abundant relevant information in
Key takeaways
- arXiv:2608.12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories.
- This scaling reflects the implicit assumption that training on longer contexts will only help the model by exposing it to richer evidence.
- We challenge this view by studying how the context window shapes a model's mode of learning, shifting it between parametric internalization and contextualization.
Why it matters
“Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
Member comments