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On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer

Summary

The paper introduces PLatour, an open-source PACT framework that connects frozen large and small models via lightweight linear bridges to balance capability and inference cost. It outperforms open-source baselines on generation tasks and surpasses Mostik's reported results on math and coding benchmarks with a smaller sender. However, controlled experiments show gains can be reproduced by uninformative sender signals, self-communication, or receiver-only training, prompting the proposal of PACT-Check to quantify genuine communication gains.

SourcearXiv Computational LinguisticsAuthor: Zhenyi Shen, Junru Lu, Wei Liu, Jiangnan Ye, Jiazheng Li, Di Yin, Xing Sun, Tameem Adel, Yali Du, Lin Gui, Yulan He
On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer
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[Submitted on 4 Nov 2026]

Title:On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer

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Abstract:Prefill-to-Autoregressive Cross-model Transfer (PACT), a form of latent communication between language models, combines a larger model's prompt representations with a smaller model's efficient generation, offering a promising way to balance capability and inference cost. However, existing systems provide limited support for independently evaluating large-small model collaboration, and improved task performance alone does not establish whether the receiver benefits from the sender's information. We introduce PLatour, an open-source PACT framework that connects frozen models through lightweight, zero-initialized linear bridges trained directly with task rewards and supports hybrid-attention architectures. PLatour significantly outperforms open-source baselines on generation tasks and surpasses Mostik's reported results on shared mathematics and coding benchmarks using a substantially smaller sender. However, controlled experiments show that the gains on some tasks can be reproduced by uninformative sender signals, self-communication, or receiver-only training. We find that trainable bridges can act as parameter-efficient adaptation mechanisms without exploiting useful sender information. We therefore propose PACT-Check to quantify how much of a trained PACT system's gain genuinely comes from communication. Finally, we discuss the opportunities and challenges of PACT systems. Code is available at this https URL.

Comments: 35 pages

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2611.06976 [cs.CL]

(or arXiv:2611.06976v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2611.06976

arXiv-issued DOI via DataCite

Submission history

From: Zhenyi Shen [view email] [v1] Wed, 4 Nov 2026 13:33:47 UTC (467 KB)

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Key points

  • PLatour is an open-source PACT framework linking frozen models through zero-initialized linear bridges trained with task rewards, supporting hybrid-attention architectures.
  • Controlled experiments reveal that task gains may stem from bridges acting as parameter-efficient adaptation rather than exploiting useful sender information.
  • PACT-Check is proposed to measure how much of a trained PACT system's gain truly comes from communication.

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