推理模型中的流体表示
Fluid Reasoning Representations
AI总结：
研究揭示了推理模型通过上下文细化令牌表示提升抽象问题解决能力的机制，提出流体推理表示概念。
摘要：
推理语言模型通过生成长链的思考过程，在抽象问题上显著优于非推理语言模型。然而，使这种卓越性能得以实现的内部模型机制仍不明确。我们对QwQ-32B模型进行了机理分析，该模型专门训练以生成详尽的推理轨迹。在Mystery Blocksworld领域，一个语义模糊的规划环境，我们发现QwQ-32B在推理过程中逐渐改进了其对动作和概念的内部表示。模型发展出关注结构而非具体动作名称的抽象编码。通过引导实验，我们建立了因果证据，证明这些适应性提高了问题解决能力：注入来自成功轨迹的精细表示可提升准确性，而符号表示可将许多模糊编码替换为最小性能损失。我们发现推动推理模型性能的一个因素是在上下文中的令牌表示细化，我们将其称为流体推理表示。
英文摘要：
Frontier large language models increasingly solve complex tasks involving abstract concepts through extended test-time thinking. Yet we lack a mechanistic account of how extended thinking changes hidden-state representations over the course of a reasoning trace. We introduce \textit{Fluid Reasoning Representations} (FRRs), a representation-level account of how LLMs organize action and predicate concepts during self-generated reasoning, and test them on obfuscated planning, symbolic, and mathematical tasks where task-relevant words are replaced while problem structure is preserved. Across open-weight base, instruction-tuned, and extended-thinking LLMs, representations of the same action or predicate become more similar across wordings and move toward the corresponding unobfuscated concepts over the reasoning trace. Causal probes show that these representations affect behavior: cross-naming steering improves held-out accuracy beyond Gaussian and shuffled controls, symbolic patching retains more action information than shuffled patching, and subtracting refined directions degrades accuracy; together, these results suggest that extended thinking strengthens a representation dynamic already present at lower magnitude in base and instruction-tuned LLMs. Our codebase is open-sourced \href{https://github.com/AI4Collaboration/Fluid-Reasoning-Representation}{here}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
