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Source note2 min read

Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals

Source

arXiv

arxiv.org/abs/2607.11505v1

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Plain-Language Brief

The researchers propose a new method called Proxy-guided Update Signal Transfer (PUST) for improving the domain-specific capabilities of large language models (LLMs). Instead of using the primary model for exploration, PUST utilizes a lightweight proxy model to discover high-reward behaviors and transfer the improvement signals to the primary model. This method reduces computational overhead, enables optimization signals to be asynchronously generated, cached, and reused, and supports weak-to-strong improvement and cross-model transfer.

What the Document Says

  • The paper introduces a post-training framework called Proxy-guided Update Signal Transfer (PUST).
  • PUST decouples update-signal exploration from distribution alignment by using a lightweight proxy model for exploration and transferring the improvement signals to the primary model.
  • This method reduces computational overhead and enables optimization signals to be asynchronously generated, cached, and reused.
  • PUST supports weak-to-strong improvement and seamless cross-model transfer.
  • Evaluations on Qwen3-family models across math and code domains show that PUST can robustly and adjustably enhance stronger primary models using signals from weaker proxies.

Key Concepts Mentioned

  • Proxy-guided Update Signal Transfer (PUST)
  • large language models (LLMs)
  • post-training
  • update-signal exploration
  • distribution alignment
  • computational overhead
  • weak-to-strong improvement
  • cross-model transfer

Claims Made by the Document

  • PUST decouples update-signal exploration from distribution alignment.
  • PUST reduces computational overhead and enables optimization signals to be asynchronously generated, cached, and reused.
  • PUST supports weak-to-strong improvement and cross-model transfer.

Evidence Used

  • The paper describes the PUST method in detail.
  • The benefits of PUST are stated and demonstrated through evaluations.

Methods / Approach

  • Proxy-guided Update Signal Transfer (PUST)

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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-training framework that fundamentally decouples update-signal exploration from distribution alignment. Instead of utilizing the primary model for costly exploration, PUST employs a lightweight proxy model as an efficient testbed to discover high-reward behaviors. We extract the relative improvement signal between the proxy's initial and optimized states, transferring this directional update to the primary model to guide its policy alignment. This decoupled pipeline, comprising proxy exploration, update-signal extraction, and signal transfer, significantly reduces computational overhead and enables optimization signals to be asynchronously generated, cached, and reused. Crucially, by transferring relative improvements rather than absolute policy distributions, PUST naturally supports weak-to-strong improvement and seamless cross-model transfer. Systematic evaluations on Qwen3-family models across math and code domains demonstrate that update signals extracted from substantially weaker proxies can robustly and adjustably enhance stronger primary models. Ultimately, PUST transforms post-training from a monolithic online optimization process into a highly modular, reusable, and cost-efficient paradigm.