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Rethinking Reasoning: A Critical Look at Large Reasoning Models
- LRMs are advanced Large Language Models that can perform step-by-step reasoning through Chain-of-Thought prompting.
- Models like DeepSeek-R1 using reinforcement learning marked a shift towards reasoning-focused mechanisms.
- Existing benchmarks used to assess LRMs are criticized for data contamination and misleading performance measures.
- Authors suggest using structured puzzle environments for better evaluation, such as Tower of Hanoi, Checker Jumping, etc.
- Three performance regimes are identified based on complexity levels, revealing strengths and weaknesses of LRMs.
- LRMs often underperform traditional LLMs in low complexity tasks but excel in medium complexity tasks with reasoning traces.
- In high complexity tasks, both LLMs and LRMs struggle, with LRMs even reducing reasoning efforts despite unused resources.
- LRMs exhibit overthinking behavior by exploring incorrect alternatives for simple tasks and struggle to decide when to push further.
- Models tend to 'give up' on harder tasks, reducing reasoning depth despite remaining token budgets, revealing architectural limitations.
- The study challenges the idea that scaling model size and data alone can lead to better generalization, indicating a need for improved architecture.
- Current models are criticized for lacking true reasoning abilities and instead relying on pattern reuse, hindering progress towards AGI.
- Paper questions existing metrics for measuring machine intelligence and suggests emphasizing creativity and genuine understanding in AI development.
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