Randomized YaRN Improves Length Generalization for Long-Context Reasoning
Large language models (LLMs) are typically pretrained on short sequences and then extended to work on longer sequences with additional training. However, such LLMs still struggle to further generalize to very long sequences. We propose Randomized YaRN, a training method that improves length generalization by combining YaRN-based positional extrapolation with randomized positional encoding and a length curriculum. During training on short context data, tokens are assigned YaRN positional encodings sampled from a larger position range, exposing the model to out-of-distribution positional representations even on short-context inputs. We evaluate Randomized YaRN on three challenging long-cont...
| Authors | Manas Mehta, Fangcong Yin, Greg Durrett |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 30 August 2026 |
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