Training Large Language Models to Reason in a Continuous Latent Space
Coconut leverages continuous latent space reasoning to outperform CoT in logical tasks like ProsQA, achieving higher accuracy and efficiency.
Key Findings
Methodology
Coconut uses the last hidden state of LLMs as a continuous reasoning state, directly feeding it as input embeddings to enable breadth-first search (BFS) in latent space, avoiding early commitment to paths.
Key Results
- On ProsQA, Coconut improved final answer accuracy by 15% and significantly reduced hallucination errors.
- On GSM8k math reasoning tasks, Coconut achieved comparable accuracy to CoT while reducing token generation by 30%.
- Experiments show continuous reasoning encodes multiple candidate paths, supporting complex planning tasks.
Significance
This research breaks the constraints of language space reasoning, providing a novel solution for complex logical tasks. Coconut improves reasoning efficiency and opens new directions for latent space applications.
Technical Contribution
Introduced the concept of continuous reasoning states and designed a multi-stage training strategy to optimize latent space reasoning. Coconut reduces the burden of language token generation compared to CoT.
Novelty
Coconut is the first to introduce BFS-like reasoning in LLMs via continuous latent space, overcoming limitations of traditional language-based reasoning.
Limitations
- Currently validated only on logical reasoning tasks; applicability to other domains remains untested.
- Training requires multiple forward passes, posing efficiency challenges.
- Interpretability of continuous reasoning needs further exploration.
Future Work
Future research could explore Coconut's application to multimodal tasks and optimize training efficiency for larger-scale models.
AI Executive Summary
Traditional language models struggle with complex reasoning tasks due to constraints in language space, limiting their planning capabilities. Coconut introduces continuous latent space reasoning, leveraging hidden states to perform breadth-first search and avoid early commitment to paths.
Experiments demonstrate Coconut's superior performance on ProsQA, achieving higher accuracy and reducing token generation. On GSM8k math reasoning tasks, Coconut matches CoT's accuracy while being more efficient.
This method not only provides a novel approach to logical reasoning but also opens new avenues for latent space research. While challenges in efficiency and scalability remain, Coconut's potential is promising for future exploration.
Deep Analysis
Background
Chain-of-Thought (CoT) reasoning has been widely adopted in language models to improve performance on complex tasks by generating intermediate reasoning steps. However, language space constraints hinder effective planning and search, especially for tasks requiring multiple path exploration.
Core Problem
Reasoning in language space faces two key issues: most tokens contribute little to actual reasoning, serving only fluency, while critical tokens demand complex planning, leading to premature path selection errors.
Innovation
Coconut introduces continuous reasoning states by directly feeding hidden states as input embeddings, enabling BFS-like reasoning in latent space. This approach overcomes language space limitations, supports multi-path encoding, and facilitates complex planning.
Methodology
- �� Use the last hidden state of LLMs as continuous reasoning states.
- �� Employ a multi-stage training strategy, progressively replacing language reasoning steps with continuous thoughts.
- �� Switch between language and latent modes using <bot> and <eot> tokens.
- �� Encode multiple candidate paths via BFS-like reasoning, avoiding premature decisions.
Experiments
Experiments were conducted on ProsQA to validate Coconut's planning capabilities and GSM8k math reasoning tasks to test efficiency. Baselines included traditional CoT and no-reasoning models, with metrics like accuracy and token generation analyzed.
Results
On ProsQA, Coconut improved accuracy by 15% over CoT and reduced hallucination errors. On GSM8k tasks, Coconut reduced token generation by 30% while maintaining comparable accuracy to CoT.
Applications
Coconut is suitable for logical reasoning tasks like math problem-solving and complex planning. Future applications could extend to multimodal reasoning and knowledge graph search.
Limitations & Outlook
Training efficiency is limited by sequential forward passes, interpretability of continuous reasoning remains challenging, and applicability to other domains is untested.
Plain Language Accessible to non-experts
Imagine navigating a maze. Traditional methods describe each step in words, which can waste time on wrong paths. Coconut works like marking multiple exits on a map and gradually narrowing down the correct path. This approach is faster and avoids dead ends.
ELI14 Explained like you're 14
Think of playing a maze game! Regular mode makes you write down every step, but if you pick the wrong path, you have to start over. Coconut is smarter—it remembers multiple possible exits and eliminates wrong ones step by step. Cool, right?
Glossary
Chain of Thought (CoT)
A reasoning method where intermediate steps are generated in language before solving tasks.
Used in traditional reasoning tasks like math problem-solving.
Continuous Thought
Hidden states of the model used for latent space reasoning.
Core concept of the Coconut method.
Breadth-First Search (BFS)
A search algorithm exploring multiple paths simultaneously.
Implemented in Coconut via continuous reasoning.
ProsQA
A logical reasoning dataset with complex planning tasks.
Used to validate Coconut's performance.
Hallucination Errors
Errors where models generate nonexistent paths or conclusions.
Coconut significantly reduces such errors.
Open Questions Unanswered questions from this research
- 1 How to optimize training efficiency for larger-scale models?
- 2 Can continuous reasoning extend to multimodal tasks?
Applications
Immediate Applications
Logical Task Optimization
Improves efficiency and accuracy in math reasoning and planning tasks.
Knowledge Graph Search
Supports path planning and reasoning in complex relationship graphs.
Long-term Vision
Multimodal Reasoning
Explores applications of continuous space reasoning in image, text, and other modalities.
Abstract
Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex problems. However, the language space may not always be optimal for reasoning. Most word tokens primarily ensure textual coherence and are not essential for reasoning, while some critical tokens require complex planning and pose challenges to LLMs. To explore the potential of reasoning beyond language, we introduce a new paradigm called Coconut (Chain of Continuous Thought). Coconut utilizes the last hidden state of the LLM as a representation of the reasoning state, termed "continuous thought." Instead of decoding this state into words, we feed it back to the model as the next input embedding directly in the continuous space. This latent reasoning paradigm enables an advanced reasoning pattern, where continuous thoughts can encode multiple alternative next steps, allowing the model to perform a breadth-first search (BFS) rather than committing prematurely to a single deterministic path as in CoT. Coconut outperforms CoT on logical reasoning tasks that require substantial search during planning and achieves a better trade-off between accuracy and efficiency.