From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step

An Overview of "Internalizing Chain-of-Thought to Improve Latent Reasoning in Language Models"

The paper under review explores an innovative approach to enhance the reasoning capabilities of language models (LMs) by internalizing the chain-of-thought (CoT) reasoning process, which is traditionally explicit. The authors propose a method they refer to as "Stepwise Internalization," wherein a language model trained for explicit CoT reasoning is gradually stripped of these intermediate steps through a systematic finetuning process. This internalization is posited to simplify the reasoning process and improve performance on complex tasks without the computational overhead of generating explicit intermediate steps.

Introduction

The prevailing methodology for improving LM performance on complex reasoning tasks involves explicit CoT reasoning. This method, although effective, can be computationally intensive, especially for problems requiring lengthy reasoning chains. The authors examine whether LMs can efficiently internalize intermediate reasoning steps into their hidden states, thereby bypassing the need for explicit CoT reasoning during inference.

Stepwise Internalization Methodology

The authors introduce Stepwise Internalization as their primary method for internalizing CoT steps. The process begins with a model trained using explicit CoT reasoning. During subsequent finetuning, intermediate steps are progressively removed, obliging the model to incorporate these steps into its hidden states. This method posits that by the end of the finetuning process, the model is capable of performing implicit CoT reasoning.

Empirical Evaluation and Results

The methodology was tested on two primary reasoning tasks: multi-digit multiplication and grade-school math problems (GSM8K dataset). The results were compelling. For instance, using a GPT-2 Small model, Stepwise Internalization achieved a 99% accuracy on 9-by-9 multiplication tasks, significantly outperforming standard training, which fails to solve beyond 4-by-4 multiplication. Additionally, a larger model, Mistral 7B, achieved over 50% accuracy on GSM8K without generating intermediate steps, outperforming the much larger GPT-4 model in similar conditions.

Key Experiments

Dataset and Models

The datasets used include synthetic data for large multiplication problems and the GSM8K for grade-school math. Language models evaluated ranged from GPT-2 Small to Mistral 7B, each tested on their ability to perform implicit CoT reasoning.

Baselines

The paper compares several baselines:

1. No CoT: Models directly trained without any intermediate reasoning steps.
2. Explicit CoT: Models finetuned or prompted with explicit reasoning steps.
3. ICoT-KD (Implicit CoT via Knowledge Distillation): Distillation of a teacher model's hidden state into a student model.

Analysis and Ablation Studies

The authors conducted thorough ablation studies to validate the robustness of their approach. These studies examined the effects of specific components of their methodology, such as removal smoothing and optimizer resetting. Results from these ablations underscored the significance of these techniques in maintaining training stability and ensuring convergence.

Implications and Future Work

The implications of this work are substantial for both practical applications and theoretical advancements in AI. Practically, it allows for faster inference in real-world applications by removing the need for lengthy reasoning chains, thus saving computational resources and latency. Theoretically, it emphasizes an innovative way of leveraging the hidden states of language models to internalize complex reasoning processes.

However, several limitations were noted. The training cost remains high due to the iterative nature of the finetuning process. Furthermore, while implicit CoT reasoning provides speed advantages, it still lags behind explicit CoT in raw accuracy, signaling avenues for further research to close this gap. Additionally, the lack of interpretability due to the internalized reasoning steps poses a challenge, which could potentially be addressed through future advances in model probing techniques.

Conclusion

The authors successfully demonstrate the feasibility and efficacy of Stepwise Internalization, providing a promising direction for future research aimed at improving the latent reasoning abilities of language models. Their method balances the trade-off between computational efficiency and accuracy, making it a valuable addition to the toolkit for advanced AI research.

Future work might explore scaling this approach to even larger and more complex reasoning tasks, optimizing the training process to reduce computational overhead, and improving the interpretability of the internalized reasoning steps. The full potential of internalized CoT reasoning remains an exciting frontier in the evolution of language model capabilities.