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🧵 1/8 The Illusion of Thinking: Are reasoning models like o1/o3, DeepSeek-R1, and Claude 3.7 Sonnet really "thinking"? 🤔 Or are they just throwing more compute towards pattern matching? The new Large Reasoning Models (LRMs) show promising gains on math and coding benchmarks,… https://t.co/Ah14a7fwkg
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"full_text": "🧵 1/8 The Illusion of Thinking: Are reasoning models like o1/o3, DeepSeek-R1, and Claude 3.7 Sonnet really \"thinking\"? 🤔 Or are they just throwing more compute towards pattern matching?\n\nThe new Large Reasoning Models (LRMs) show promising gains on math and coding benchmarks,… https://t.co/Ah14a7fwkg",
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"text": "🧵 2/8 The evaluation problem for reasoning 📊\n\nMost studies judge reasoning by final answer accuracy on popular math & coding benchmarks. Those benchmarks are:\n- Sometimes leaked into training data \n- Fixed in difficulty and hard to control\n- Blind to what happens inside the chain-of-thought.\n\nSo, we can NOT easily tell if improvements come from better reasoning or just more compute + memorization, or a synthesized self-reflection. We needed a cleaner experimental and evaluation setup.",
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"text": "🧵 3/8 Controllable puzzle environments 🧩\n\nWe designed experiments using Tower of Hanoi, Checker Jumping, River Crossing, and Blocks World. These puzzles let us:\n✅ Control complexity precisely\n✅ Avoid contamination\n✅ Analyze both final answers AND reasoning traces\n\nAdditionally, we compared each “thinking” LRM with its non-thinking LLM twin:\n- Claude-3.7-Sonnet-Thinking vs Claude-3.7-Sonnet\n- DeepSeek-R1 vs DeepSeek-V3",
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"full_text": "@MFarajtabar Thanks for this. … worth exploring whether you manipulate complexity or something we might call ‘load’ - the problem type is a complexity manipulation itself. What’s the role of contamination in exposure to eg T of H? Are you testing enough of the reasoning domain to generalise?",
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