Papers with LLM decoding
Instantly Learning Preference Alignment via In-context DPO (2025.naacl-long)
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| Challenge: | Presently, mainstream approaches to HPA heavily depend on fine-tuning . however, the huge computational and annotation costs of fine-timing are hard to ignore . |
| Approach: | They propose a tuning-free approach to HPA using LLMs' decoding . they first rethink the derivation procedures of DPO and build an instant scorer . |
| Outcome: | The proposed approach outperforms existing methods even with tuning-free baselines and an upgraded scorer. |
Martingale Foresight Sampling: A Principled Approach to Inference-Time LLM Decoding (2026.eacl-long)
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| Challenge: | Standard autoregressive decoding in large language models is short-sighted, often failing to find globally optimal reasoning paths due to token-by-token generation process. |
| Approach: | They propose a principled framework that reformulates LLM decoding as a problem of identifying an optimal stochastic process. |
| Outcome: | The proposed framework surpasses state-of-the-art methods in accuracy while significantly improving computational efficiency. |
TokenSkip: Controllable Chain-of-Thought Compression in LLMs (2025.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose a chain-of-thought (CoT) prompting approach that enables LLMs to selectively skip less important tokens, allowing for controllable CoT compression. |
| Outcome: | Experiments show that TokenSkip reduces CoT token usage while preserving strong reasoning performance. |
RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding (2026.findings-acl)
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| Challenge: | Existing methods for decoding large language models generate one token per step, causing high inference latency. |
| Approach: | They propose a method that integrates retrieved exact patterns with logit-driven future cues. |
| Outcome: | Experiments on Spec-Bench, HumanEval, and MGSM-ZH show that RACER outperforms training-free methods and accelerates inference. |