The End of Manual Decoding: Towards Truly End-to-End Language Models
Authors: name: Zhichao Wang, name: Dongyang Ma, name: Xinting Huang, name: Deng Cai, name: Tian Lan, name: Jiahao Xu, name: Haitao Mi, name: Xiaoying Tang, name: Yan Wang
Organization: Tencent

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.