Reasoning Decoding Algorithms

Reasoning Decoding Algorithms

Reasoning decoding algorithms are the use of modified inference decoding algorithms to mimic multi-step reasoning algorithms, such as Chain-of-Thought. Hence, these are also called "Chain-of-Thought decoding." This is an interesting new area of AI research that aims to achieve the goals of the smart-but-slow "reasoner" models, which use multiple steps of inference computations to achieve advanced problem solving capabilities.

The basic idea is that the multiple alternative pathways that are not taken during decoding are somwhat similar to alternative lines of reasoning. More detailed analysis of these different pathways during the inference decoding phase can achieve reasoning capabilities more extensive than simple decoding algorithms. The advantage of this "reasoning decoding" method is efficiency, because multiple pathways can be examined in a single inference step.

The idea of tracking a "tree" of decoding paths is not new, and is in fact the basis of the "beam search" decoding algorithm. However, the latest research on CoT decoding takes this a step further and attempts to evaluate the correctness of multiple pathways at a higher level.

Here are the latest papers on "CoT decoding" ideas:

1. Xuezhi Wang, Denny Zhou, 23 May 2024 (v2), Chain-of-Thought Reasoning Without Prompting, https://arxiv.org/abs/2402.10200
2. xjdr-alt, Dec 2024, entropix: Entropy Based Sampling and Parallel CoT Decoding, https://github.com/xjdr-alt/entropix

Read more about the research on:

• CoT optimizations
• Decoding algorithms
• Beam search decoding
• Tree decoding algorithms
• Reasoning algorithms
• Reasoning decoding algorithms
• Multi-step reasoning algorithms
• Chain-of-Thought (CoT)
• Reasoning Inference Optimization (RIO)

Reasoning and CoT Token Efficiency Topics

Blog articles on reasoning efficiency:

• Reasoning Inference Optimization
• Reasoning Decoding

More research information on general efficiency optimization techniques for reasoning models:

• Reasoning inference optimization (RIO)
• Chain-of-Thought (CoT) optimization
• Small Reasoning Models (SRMs)
• Adaptive Inference Time Compute
• Hybrid Reasoning Models
• Reasoning Tokens

Efficiency optimizations to Chain-of-Thought include:

• Hidden Token Chain-of-Thought (HCot)
• Continuous Chain-of-Thought (Coconut)
• Chain of Draft (CoD)
• CoT Reasoning Decoding
• Concise Chain-of-Thought
• CoT Token Reduction
• CoT Step Skipping
• CoT Early Stopping
• CoT Path Reduction
• Constrained Chain-of-Thought

More AI Research Topics

Read more about:

• « Aussie AI Blog Index
• Inference Optimizations
• Reasoning Inference Optimizations
• List of Optimization Techniques
• AI Research Home

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