EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
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Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
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Recovering Hidden Reward in Diffusion-Based Policies
EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
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The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.