Humans exhibit abstraction learning consistent with prospective compression of future tasks in non-stationary domains, unlike retrospective compression algorithms or LLM-based approaches.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
roles
background 3polarities
background 3representative citing papers
DEFT dynamically generates LLM-proposed biologically-informed features during decision tree construction to achieve interpretable and predictive DNA sequence classification.
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
Denoising Recursion Models train multi-step noise reversal in looped transformers and outperform the prior Tiny Recursion Model on ARC-AGI.
An adaptive compute-optimal strategy for scaling LLM test-time compute achieves over 4x efficiency gains versus best-of-N and lets smaller models outperform 14x larger ones on some problems.
Quiet-STaR lets language models learn token-level rationales from general text, producing zero-shot gains on GSM8K and CommonsenseQA after continued pretraining.
citing papers explorer
-
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
An adaptive compute-optimal strategy for scaling LLM test-time compute achieves over 4x efficiency gains versus best-of-N and lets smaller models outperform 14x larger ones on some problems.
-
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Quiet-STaR lets language models learn token-level rationales from general text, producing zero-shot gains on GSM8K and CommonsenseQA after continued pretraining.