提升隐式思维链的稳定性与效率，让大模型推理更准确且不增加计算开销。
SIM-CoT: Supervised Implicit Chain-of-Thought
- 引入步骤级监督机制，防止隐式推理过程中的表征退化。
- 在GPT-2上使Coconut性能提升8.2%，在LLaMA-3.1 8B上使CODI提升3.0%。
- 保持隐式推理高效性，同时实现可解释的逐步可视化分析。
隐式思维链（Implicit CoT）方法为大语言模型提供了高效的推理路径，但其性能始终落后于显式思维链。我们发现，当增加隐式推理的计算量时，训练常因潜在表征趋于同质而崩溃，根源在于当前方法缺乏步骤级监督。为此，我们提出SIM-CoT，一个可插拔的训练模块，在训练中通过辅助解码器将每个隐式令牌与对应的显式推理步骤对齐，从而稳定并丰富潜空间。该模块在推理阶段移除，不增加额外开销。它还能将每个潜表示投影到显式推理词汇，实现逐步可视化与诊断。SIM-CoT显著提升隐式方法的域内准确率和跨域稳定性：在GPT-2上使Coconut提升+8.2%，在LLaMA-3.1 8B上使CODI提升+3.0%；且在相同条件下，优于显式思维链基线2.1%，同时节省2.3倍的推理令牌。代码已开源。
原文摘要 · Abstract (English)
Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited their adoption. We identify a core latent instability issue when scaling the computational budget of implicit CoT: as the number of reasoning tokens increases, training often becomes unstable and collapses. Our analysis shows that this instability arises from latent representations becoming homogeneous and losing semantic diversity, caused by insufficient step-level supervision in current implicit CoT methods. To address this, we propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space. SIM-CoT employs an auxiliary decoder during training to align each implicit token with its corresponding explicit reasoning step, ensuring latent states capture distinct and meaningful information. The auxiliary decoder is removed at inference, preserving the efficiency of implicit CoT with no added overhead. It also provides interpretability by projecting each latent token onto an explicit reasoning vocabulary, enabling per-step visualization and diagnosis. SIM-CoT significantly improves both in-domain accuracy and out-of-domain stability of implicit CoT methods, boosting Coconut by +8.2\% on GPT-2 and CODI by +3.0\% on LLaMA-3.1 8B. It further surpasses the explicit CoT baseline on GPT-2 by 2.1\% with 2.3$\times$ greater token efficiency, while closing the performance gap on larger models like LLaMA-3.1 8B. Code: https://github.com/InternLM/SIM-CoT
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