{"slug": "on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model", "title": "On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer", "summary": "A new arXiv paper (2611.06976v1) introduces PLatour, an open-source Prefill-to-Autoregressive Cross-model Transfer (PACT) framework that links frozen language 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, but controlled experiments show the gains on some tasks can be reproduced by uninformative sender signals, self-communication, or receiver-only training. The authors propose PACT-Check to quantify how much of a trained PACT system's gain genuinely comes from communication, finding that trainable bridges can act as parameter-efficient adaptation mechanisms without exploiting useful sender information; code is available at github.com/zhenyi4/platour.", "body_md": "arXiv:2611.06976v1 Announce Type: new \nAbstract: 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 https://github.com/zhenyi4/platour.", "url": "https://wpnews.pro/news/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model", "canonical_source": "https://arxiv.org/abs/2611.06976", "published_at": "2026-11-06 05:00:00+00:00", "updated_at": "2026-11-06 05:19:21.731807+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "ai-research", "ai-infrastructure"], "entities": ["PLatour", "PACT", "PACT-Check", "Mostik", "arXiv", "zhenyi4"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model", "markdown": "https://wpnews.pro/news/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model.md", "text": "https://wpnews.pro/news/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model.txt", "jsonld": "https://wpnews.pro/news/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model.jsonld"}}