| 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
