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Introduces DAIS, a training-time framework that converts chain-of-thought rationales into dependency-conditioned intermediate QA records to provide better supervision for complex reasoning. Achieves up to 5.6% accuracy improvement over baselines on policy-compliance benchmarks.
The author reflects that the primary bottleneck in running AI agents is not the model's capability but the human's ability to precisely define what 'good' or 'done' means, drawing parallels to managing people.
This paper proposes DARS, a framework that constructs routing supervision from a distributional view of model behavior to address the unreliability of single-shot labels in LLM routing.
Trust functions enable near-lossless weak-to-strong generalization by identifying reliable weak labels for training, achieving performance comparable to ground-truth supervision across multiple domains.
GEM introduces a generative supervision method to improve embodied intelligence by leveraging generative models for training.
A reflection on the gap between impressive AI agent demos and dependable real-world execution, arguing that current agents excel at structured tasks but fail under unpredictable conditions, suggesting near-term AI roles will focus on narrow automation with human oversight.