Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost

TL;DR

Post-Reasoning enhances instruction-tuned models by conditioning them to justify answers after response generation, improving performance without additional latency or token cost across diverse benchmarks.

Contribution

It introduces Post-Reasoning, a method that improves model performance by post-hoc justification conditioning, and demonstrates its effectiveness through extensive evaluation and supervised tuning.

Findings

Post-Reasoning improves performance in over 88% of settings.

Achieves a mean relative improvement of 17.37%.

Supervised post-reason tuning further enhances results, exceeding baseline by 8.01%.

Abstract

As the widespread adoption of Large Language Models (LLMs) accelerates, token consumption from intermediate reasoning traces increasingly contributes to inference latency and operational cost. Recent studies suggest that many real-world tasks require little to no explicit reasoning, with additional reasoning sometimes even degrading performance. In this work, we propose \textbf{Post-Reasoning}, a simple yet effective approach that improves instruction-tuned models by conditioning them to justify their answers after generating the final response. By design, it enables the final answer to be obtained without additional latency or token cost, while still improving performance through simple instruction augmentation. We evaluate Post-Reasoning across \(117\) model--benchmark settings spanning \(13\) open and proprietary models, \(4\) model families, and \(9\) diverse reasoning and knowledge-intensive benchmarks, including AMC, HMMT, GSM8K, GPQA, MMLU-Pro, and BIG-Bench Hard. Post-Reasoning improves performance in over \(88.19\%\) of evaluated settings, achieving a mean relative improvements of \(17.37\%\). Furthermore, we propose supervised post-reason tuning, which further improves performance in over \(91.11\%\) of evaluated settings, and exceeds the prompt-based post-reasoning baseline by an average of \(8.01\%\), demonstrating that post-reasoning can be effectively internalized through training. Ultimately, Post-Reasoning establishes a new performance ceiling for direct-answer capabilities.