Abstract
Chain-of-Thought (CoT) reasoning could in principle enable a deeper understanding of a language model's (LM) internal reasoning. However, prior work suggests that some LMs answer questions similarly despite changes in their CoT, suggesting that those models are not truly using the CoT. We propose a training method to produce CoTs that are sufficient alone for predicting future text, independent of other context. This methodology gives a guarantee that if the LM can predict future tokens, then it must have used the CoT to understand its context. We formalize the idea that the truthfulness of a sender to a receiver LM is the degree to which the sender helps the receiver predict their future observations. Then we define a "Markovian" LM as one which predicts future text given only a CoT as context. We derive a "Markovian training" procedure by applying our definition of truthfulness to a Markovian LM and optimizing via policy gradient and Proximal Policy Optimization (PPO). We demonstrate the effectiveness of our training algorithm on long-context arithmetic problems, show that the model utilizes the CoT, and validate that the generated CoT is meaningful and usable by other models.
Overview
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The paper introduces a new training methodology for language models, emphasizing the generation of Chain-of-Thought (CoT) reasoning that genuinely reflects the model’s internal thought processes.
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Markovian Language Models are described as models that use only the CoT as context for future predictions, with the novel 'Markovian Training' method enhancing this by using techniques such as policy gradient and Proximal Policy Optimization.
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The training's effectiveness was validated through its application in arithmetic problem-solving, proving that generated CoTs are crucial, interpretable, and transferable for the model’s reasoning, paving the way for transparent AI systems.
Exploring "Markovian Training" for Language Models' Chain-of-Thought Reasoning
Introduction to Chain-of-Thought Reasoning Challenges
The idea of using a language model’s (LM) natural language capabilities to explain its reasoning process seems intuitive. This leads to what's known as Chain-of-Thought (CoT) prompting, where we expect the LM to provide a step-by-step explanation of its thought process before arriving at an answer. However, a key issue persists: how can we be sure that the CoT provided by the LM truly reflects its internal reasoning mechanism?
Previous studies have shown that simply changing the CoT does not always affect the final result given by the LM, suggesting that the CoT may not truly represent the LM's reasoning process. Addressing this, the paper introduces an innovative training method for LMs focused on generating meaningful and impactful CoTs that act as genuine markers of the LM's thought process.
Key Concept: Markovian Language Models and Training
Defining Markovian Language Models:
- A Markovian LM is defined as one which predicts future text, like answers to questions, using only the CoT as the context. This approach aims to ensure that the memory or state of the LM contains only tokens pertinent to future predictions, effectively transforming the CoT into a self-sufficient predictive tool.
"Markovian Training" Methodology:
- The paper proposes a novel training regimen leveraging both policy gradient and Proximal Policy Optimization (PPO) to optimize the generation of CoT tokens. This training ensures that the LM's predictions are solely based on its CoT, confirming that the CoT is integral to its reasoning process.
Empirical Validation
Achievements in Arithmetic Problem-Solving:
- The effectiveness of the Markovian training approach was evaluated on long-context arithmetic problems. The results demonstrated that the LM could utilize its generated CoTs effectively during inference sessions, confirming that these CoTs are crucial for its reasoning.
Validation of CoT's Meaningfulness:
- Beyond just utilizing CoTs for its internal processes, it was found that these generated CoTs are interpretable and transferable, meaning other models could understand and leverage them without access to the original LM's internal state. This marks significant progress in creating universally comprehensible machine reasoning steps.
Theoretical Contributions and Practical Implications
The paper emphasizes the potential for more transparent AI systems and enhances our ability to trust and understand decisions made by AI, particularly in scenarios where understanding the rationale behind a decision is as critical as the decision itself.
Future Speculations
Looking forward, the idea of solely relying on generated CoT for predictions could pave the way to more robust forms of machine reasoning where the reasoning process itself is subjected to scrutiny and improvement. This could be fundamental for applications in fields where decisions need clear justifications, like medicine or law.
In conclusion, the exploration of Markovian Training sets an exciting precedent for developing LMs that not only answer questions but provide a window into their thought process transparently and reliably.
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