Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals
Post-training is essential for refining the domain-specific capabilities of large language models (LLMs), yet existing reward optimization and distribution matching methods tightly couple policy exploration with distribution alignment. This coupling forces expensive exploration directly on the policy model and severely hinders the asynchronous generation, reuse, and cross-model transfer of optimization signals. In this paper, we propose Proxy-guided Update Signal Transfer (PUST), a novel post-tr
Record details
Published: 13 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (13 July 2026), “Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals,” evidence record 1509, https://ethics.ai/record/1509 (originally published by HuggingFace Daily Papers).
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