arXiv:2608. 06819v1 Announce Type: cross Abstract: Token-level collaboration allows a large language model (LLM) to assist a small language model (SLM) when their predictions diverge.
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arXiv:2601. 05106v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit strengths across diverse domains.
The paper introduces BRIDGE, a method for efficient instruction tuning of large language models that jointly selects prompt context and response supervision. BRIDGE uses a shared validation-directed interaction surrogate to evaluate token utility conditioned on the other side’s retained state, and employs budgeted alternating selection and structure-aware projection to produce coherent supervision spans. Experiments across three model families show BRIDGE outperforms independent selection methods in mathematical reasoning, code generation, and instruction following, with its advantage increasing as compression grows.
arXiv:2606. 13862v1 Announce Type: cross Abstract: Long Chain-of-Thought (CoT) reasoning improves LLM problem-solving but is computationally expensive due to sequential token generation.
arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...
arXiv:2606. 01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths.
arXiv:2610.00888v1 Announce Type: cross Abstract: Multi-token prediction (MTP) improves the throughput of autoregressive generation by enabling the language model to draft multiple next tokens per fo...