arXiv Machine Learning
Sep 11

A Fragility Spectrum for Recursive Language-Model Training

The paper investigates how recursive contamination—retraining language models on their own generated text—affects output diversity across 13 publicly released checkpoints. Using a fixed contamination protocol over five generations, the authors find a wide spread in 4‑gram diversity (0.187 to 0.940), indicating that some models collapse into repetitive fragments while others remain largely unaffected. The study shows that a model’s susceptibility to collapse is an intrinsic property of the checkpoint, not predicted by parameter scale or static indicators, and that simple interventions such as tightening top‑p sampling can significantly slow or halt collapse.

By Yangze Liu, Zhongyi Han
arXiv Machine Learning
Oct 8

When Should an In-Context Learner Expand Its Hypothesis Space?

The paper investigates when an in‑context learner should expand its hypothesis space, framing the decision as a costly sequential problem where prediction failures must be translated into structural evidence and then into an action. Using a Structural Revision Environment that allows exact Bayesian analysis, the authors find that the optimal decision to expand is governed by a value boundary rather than a simple evidence threshold, and that this boundary depends on the price of expansion, the remaining horizon, and the specific predictions that cannot be repaired locally. Experiments with transformers and language models show that while these models exhibit a failure‑sensitive signal, they often fail to incorporate cost and horizon considerations into their expansion decisions, and post‑training adjustments to priors or prediction sharpness do not alter the expansion criterion.

By Weihan Li, Xinlei Chen, Junhao Wu, Tianshi Zheng
arXiv Computation and Language
Sep 22

Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting

The paper investigates time‑incremental continued pretraining (CPT) of large language models using web‑scale data that overlaps across snapshots. Across six open‑weight models, CPT improves factual recall without catastrophic forgetting, while the cost is negligible and data quality outweighs quantity. The study identifies optimal learning rates, shows LoRA can match full CPT, and demonstrates that CPT gains transfer to fine‑tuned models.

By F{\i}rat \"Oncel, Salman Hussain Ali, Mirco Ravanelli, Cem Subakan, \c{C}a\u{g}atay Y{\i}ld{\i}z