Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations
This addresses the issue for AI researchers and practitioners by showing that tool-augmented LRMs can overcome limitations in reasoning tasks, challenging the narrative that reasoning is an illusion, though it appears incremental as it builds on existing LRM and tool-use methods.
The paper tackles the problem that Large Reasoning Models (LRMs) may not enhance reasoning ability compared to non-reasoning LLMs, and finds that with tool augmentations like Python interpreters and scratchpads, LRMs consistently outperform non-reasoning counterparts across all task complexities on Apple's benchmark puzzles.
Large Reasoning Models (LRMs) have become a central focus in today's large language model (LLM) research, where models are designed to output a step-by-step thinking process before arriving at a final answer to handle complex reasoning tasks. Despite their promise, recent empirical studies (e.g., [Shojaee et al., 2025] from Apple) suggest that this thinking process may not actually enhance reasoning ability, where LLMs without explicit reasoning actually outperform LRMs on tasks with low or high complexity. In this work, we revisit these findings and investigate whether the limitations of LRMs persist when tool augmentations are introduced. We incorporate two types of tools, Python interpreters and scratchpads, and evaluate three representative LLMs and their LRM counterparts on Apple's benchmark reasoning puzzles. Our results show that, with proper tool use, LRMs consistently outperform their non-reasoning counterparts across all levels of task complexity. These findings challenge the recent narrative that reasoning is an illusion and highlight the potential of tool-augmented LRMs for solving complex problems.