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training-data

topic4 events
papersTODAY 04:00 UTC

arXiv Paper Examines How Much Training Data Matters in On-policy Distillation

A new arXiv preprint investigates how much of the benefit from on-policy distillation actually comes from the training data used. Testing the two teacher-student pairings most often seen in practice, the authors report findings that challenge assumptions about data's role in the method. The work is positioned as a closer look at a technique that has become standard in frontier post-training pipelines.

distillation/post-training/llm/research
papersTODAY 04:00 UTC

Paper Proposes Joint Optimization of Prompts and Training Data via Failure Signals

A new arXiv paper addresses automatic prompt optimization, a technique that normally revises prompts using task feedback while leaving the training set unchanged. The authors argue that repeatedly tuning against the same examples limits feedback to already-known weaknesses, and they propose a failure-guided approach in which prompts and training data are improved together. This co-evolution aims to surface new shortcomings rather than only correcting previously identified ones.

prompt-optimization/training-data/llm/research
papersSEP 10 04:00 UTC

Study Finds Data, Not Typology, Shapes Language Models' Word Order Preferences

A new arXiv paper examines word order preferences in decoder-only language models, testing 192 artificial languages alongside typologically diverse natural languages. The authors report a consistent left-branching bias in the models and argue that training data, rather than linguistic typology, shapes these preferences. The study also explores how this bias relates to model performance on right-branching languages.

language-models/linguistics/research/nlp