VGF solves behavior-regularized RL by transporting particles from a reference distribution to the value-induced optimal policy via discrete value-guided gradient flow.
arXiv preprint arXiv:2310.04373 , url=
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Introduces AIR, an asymmetric regularization that anchors open-ended safety prompts to verifiable ones via stop-gradient, improving invariance and accuracy when combined with group preference optimization.
MORA breaks the safety-helpfulness ceiling in LLMs by pre-sampling single-reward prompts and rewriting them to incorporate multi-dimensional intents, delivering 5-12.4% gains in sequential alignment and 4.6% overall improvement in simultaneous alignment.
Random sampling matches active preference learning on win-rate gains in online DPO yet both degrade benchmark performance, making active selection's overhead hard to justify.
Discusses conditions under which maximizing proxy utilities is harmful and suggests problems for decision theory applications.
citing papers explorer
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Reinforcement Learning via Value Gradient Flow
VGF solves behavior-regularized RL by transporting particles from a reference distribution to the value-induced optimal policy via discrete value-guided gradient flow.
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Towards Context-Invariant Safety Alignment for Large Language Models
Introduces AIR, an asymmetric regularization that anchors open-ended safety prompts to verifiable ones via stop-gradient, improving invariance and accuracy when combined with group preference optimization.
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Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion
MORA breaks the safety-helpfulness ceiling in LLMs by pre-sampling single-reward prompts and rewriting them to incorporate multi-dimensional intents, delivering 5-12.4% gains in sequential alignment and 4.6% overall improvement in simultaneous alignment.
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Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
Random sampling matches active preference learning on win-rate gains in online DPO yet both degrade benchmark performance, making active selection's overhead hard to justify.
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Against Proxy Optimization
Discusses conditions under which maximizing proxy utilities is harmful and suggests problems for decision theory applications.