arXiv AI
Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap
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arXiv Machine Learning
Jun 29
EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction
arXiv:2606. 27550v1 Announce Type: cross Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto approach for self-speculative decoding.
arXiv Machine Learning
Jun 16
Simplifying the Modeling of Arbitrary Conditionals in Natural Language
arXiv:2606. 14943v1 Announce Type: cross Abstract: Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood computation.
arXiv Computation and Language
Oct 28
Reinforced Fast Weights via Next-Sequence Prediction
The paper "Reinforced Fast Weights via Next-Sequence Prediction" introduces ReFINE, a method that extends the next-token prediction objective to next-sequence prediction for fast weight architectures. ReFINE selects informative token positions, generates multi-token rollouts, assigns sequence-level rewards, and optimizes the model using Group Relative Policy Optimization. Experiments on LaCT-760M and DeltaNet-1.3B demonstrate that ReFINE consistently outperforms standard next-token prediction fine‑tuning across retrieval, long‑context question answering, and LongBench tasks.
arXiv AI
Aug 18
When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.
arXiv AI
Oct 19
Next Token or Vector? Learning How Far to Decode
The paper introduces CADENCE, a context‑adaptive decoding method that learns both what to draft and how far to draft during language generation. It uses Gaussian prediction heads to encode future token spans as continuous vectors, a shared decoder to produce tokens in parallel, and a learned router to choose the prediction length based on hidden states and Gaussian parameters. Experiments on Llama‑3.1‑8B‑Instruct show that CADENCE’s router‑selected positions achieve higher draft acceptance and up to 1.3× higher generation throughput compared to standard greedy decoding.
arXiv Machine Learning
Jul 21
Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift
arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.