Implicit CoT Distillation

• Implicit CoT Distillation is a training paradigm that enables models to internalize multi-step reasoning without generating explicit intermediate steps.
• Curriculum learning, latent-state alignment, and semantic compression are key methodologies that embed reasoning within neural representations.
• This approach reduces inference overhead and latency, making it ideal for efficiency-critical applications and resource-constrained environments.

Implicit Chain-of-Thought (CoT) Distillation refers to a family of training methodologies that enable LLMs to internalize multi-step reasoning capabilities typically induced by explicit CoT supervision, such that, at inference, no intermediate reasoning steps are generated. Instead, all reasoning computation is performed internally, yielding an answer directly and achieving near-explicit-CoT accuracy with greatly improved efficiency. This paradigm addresses the computational cost and latency imposed by explicit CoT, and reveals novel insights about how reasoning can be embedded into neural representations through curriculum learning, latent-state alignment, semantic compression, and alternative forms of distillation.

1. Formal Definition and Core Motivation

Implicit CoT Distillation denotes the process where a model, trained with supervision derived from explicit CoT traces, learns to reason without emitting intermediate step tokens at test time. Rather than generating a sequence of textual rationales, the model leverages representations and mechanisms that internalize these reasoning strategies, e.g., within hidden activations, latent tokens, or internal parameterizations.

Key motivations include:

• Reducing the inference overhead of explicit CoT decoding, which imposes substantial computational burden due to token-by-token generation.
• Aligning the reasoning process with the model's native computation, promoting efficient use of the model's representational and depth-wise capacities.
• Enabling deployment in efficiency-critical settings and with resource-constrained (small) LLMs, where explicit CoT quickly becomes infeasible (Deng et al., 2024).

2. Methodological Approaches

Implicit CoT distillation encompasses several methodological variants, outlined below.

2.1 Stepwise Internalization (Curriculum Learning)

The curriculum-based “Stepwise Internalization” method first trains a model with full explicit CoT traces. Subsequently, intermediate