The End of Manual Decoding: Towards Truly End-to-End Language Models

zhichaowang, madongyang, tim huang, Deng Cai, Tian Lan, Jiahao Xu, Haitao Mi, XiaoyingTANG, Yan Wang

The End of Manual Decoding: Towards Truly End-to-End Language Models: 89 upvotes on Hugging Face Daily Papers, #1 of 29 papers on 2025-10-31. Day-by-day upvote history. It lost 32 votes when the Hub removed votes in bulk.

The "end-to-end" label for LLMs is a misnomer. In practice, they depend on a non-differentiable decoding process that requires laborious, hand-tuning of hyperparameters like temperature and top-p. This paper introduces AutoDeco, a novel architecture that enables truly "end-to-end" generation by learning to control its own decoding strategy. We augment the standard transformer with lightweight heads that, at each step, dynamically predict context-specific temperature and top-p values alongside the next-token logits. This approach transforms decoding into a parametric, token-level process, allowing the model to self-regulate its sampling strategy within a single forward pass. Through extensive experiments on eight benchmarks, we demonstrate that AutoDeco not only significantly outperforms default decoding strategies but also achieves performance comparable to an oracle-tuned baseline derived from "hacking the test set"-a practical upper bound for any static method. Crucially, we uncover an emergent capability for instruction-based decoding control: the model learns to interpret natural language commands (e.g., "generate with low randomness") and adjusts its predicted temperature and top-p on a token-by-token basis, opening a new paradigm for steerable and interactive LLM decoding.

Paper page on Hugging Face · arXiv

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