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Looping the middle layers of MoE Transformers can save up to 18% in training FLOPs while boosting performance beyond validation loss predictions.
LLMs can create effective execution infrastructures for certain tasks, but struggle to match human-engineered solutions in critical areas like coding and search.
Transforming pre-training data with reasoning annotations boosts model performance by over 2 percentage points without altering the training objective.
Self-improvement in LLMs is not automatic; the pathway to effective learning depends critically on the task structure and the nature of the feedback received.
Vague goals can misdirect model evolution, revealing that agents often overfit to narrow self-assessments, hindering broader learning outcomes.
Despite advances in AI, even top models struggle with real-world tasks, achieving only 30% success on a benchmark grounded in market-validated workflows.
