nowait-reasoning-optimizer

name

nowait-reasoning-optimizer

description

Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.

NOWAIT Reasoning Optimizer

Implements the NOWAIT technique from the paper "Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency" (Wang et al., 2025).

Overview

NOWAIT is a training-free inference-time intervention that suppresses self-reflection tokens (e.g., "Wait", "Hmm", "Alternatively") during generation, reducing chain-of-thought (CoT) trajectory length by 27-51% without compromising model utility.

When to Use

• Deploying R1-style reasoning models with limited compute
• Reducing inference latency for production systems
• Optimizing token costs for reasoning tasks
• Working with verbose CoT outputs that need streamlining

Supported Models

Model Series	Type	Token Reduction
QwQ-32B	RL-based	16-31%
Phi4-Reasoning-Plus	RL-based	23-28%
Qwen3-32B	RL-based	13-16%
Kimi-VL-A3B	Multimodal	40-60%
QvQ-72B-Preview	Multimodal	20-30%

Important: NOWAIT works best with RL-based models. Distilled models (Qwen3-4B/8B/14B) show degraded performance when reflection tokens are suppressed.

Quick Start