The paper "Efficient Reasoning with Hidden Thinking" introduces Hidden Thinking Networks (HTNs), a novel architecture designed to enhance the efficiency of large language models (LLMs) in complex reasoning tasks. HTNs augment LLMs with a differentiable "scratchpad" that allows them to perform intermediate computations and logical steps, mimicking human thought processes during problem-solving. This hidden thinking process is learned through backpropagation, enabling the model to dynamically adapt its reasoning strategies. By externalizing and making the reasoning steps differentiable, HTNs aim to improve transparency, controllability, and efficiency compared to standard LLMs, which often struggle with multi-step reasoning or rely on computationally expensive prompting techniques like chain-of-thought. The authors demonstrate the effectiveness of HTNs on various reasoning tasks, showcasing their potential for more efficient and interpretable problem-solving with LLMs.
The arXiv preprint "Efficient Reasoning with Hidden Thinking" introduces a novel approach to enhance the efficiency and reasoning capabilities of large language models (LLMs). The authors posit that current LLMs, while demonstrating impressive performance on various tasks, often struggle with complex reasoning problems that require multiple steps or the derivation of intermediate conclusions. They argue that this limitation stems from the direct generation of output without explicitly representing the underlying thought process, akin to a "black box" approach.
The paper proposes "Hidden Thinking" as a solution, a technique that encourages LLMs to explicitly generate intermediate reasoning steps before producing the final answer. This is achieved by prompting the model to first generate a sequence of hidden thoughts, represented as natural language sentences, that reflect the logical deductions and intermediate conclusions necessary to solve the given problem. These hidden thoughts are not directly included in the final output but serve as an internal scaffold to guide the model's reasoning process. Subsequently, the model uses these hidden thoughts as the basis for generating the final answer.
The authors hypothesize that this approach offers several advantages. First, it forces the model to decompose complex reasoning problems into smaller, more manageable steps, making the overall reasoning process more transparent and potentially easier to learn. Second, it allows the model to leverage intermediate conclusions, preventing errors that might arise from attempting to generate the final answer directly. Third, it provides a mechanism for incorporating external knowledge or constraints into the reasoning process, as these can be integrated into the hidden thoughts.
The effectiveness of Hidden Thinking is evaluated through experiments on several reasoning benchmarks, including multi-hop question answering and mathematical reasoning. The results demonstrate that augmenting LLMs with Hidden Thinking leads to significant improvements in accuracy compared to baseline models that do not utilize this technique. The authors further analyze the generated hidden thoughts to gain insights into the model's reasoning process and demonstrate that Hidden Thinking encourages more structured and logical reasoning pathways. Furthermore, they explore different prompting strategies for eliciting effective hidden thoughts and investigate the impact of the number of hidden thoughts on performance.
In conclusion, the paper presents Hidden Thinking as a promising method for enhancing the reasoning abilities of LLMs by encouraging them to explicitly generate intermediate reasoning steps. The empirical results suggest that this approach leads to improved performance on complex reasoning tasks and offers a more transparent and interpretable view into the model's internal thought processes. This opens up avenues for future research on incorporating more structured reasoning mechanisms into LLMs and developing more effective prompting strategies for eliciting high-quality hidden thoughts.
Summary of Comments ( 27 )
https://news.ycombinator.com/item?id=42919597
Hacker News users discussed the practicality and implications of the "Hidden Thinking" paper. Several commenters expressed skepticism about the real-world applicability of the proposed method, citing concerns about computational cost and the difficulty of accurately representing complex real-world problems within the framework. Some questioned the novelty of the approach, comparing it to existing techniques like MCTS (Monte Carlo Tree Search) and pointing out potential limitations in scaling and handling uncertainty. Others were more optimistic, seeing potential applications in areas like game playing and automated theorem proving, while acknowledging the need for further research and development. A few commenters also discussed the philosophical implications of machines engaging in "hidden thinking," raising questions about transparency and interpretability.
The Hacker News post titled "Efficient Reasoning with Hidden Thinking" (linking to arXiv paper 2501.19201) has generated several comments discussing the concept of "hidden thinking" in large language models and its potential implications.
Several commenters delve into the idea of LLMs exhibiting behavior reminiscent of "thinking" or internal deliberation, even though their underlying mechanism is statistical pattern matching. One commenter points out the distinction between "thinking" as traditionally understood (conscious, deliberate reasoning) and the emergent behavior of LLMs, suggesting the term "thinking" may be misleading. They acknowledge the impressive capabilities of these models while emphasizing the need for a more precise understanding of their internal processes.
The discussion also touches upon the computational cost associated with this "hidden thinking." Commenters speculate about whether the observed "thinking" is an emergent property or a result of specific architectural choices within the LLMs. One user raises the question of whether this apparent deliberation is an efficient strategy for problem-solving, considering the computational resources required.
Another commenter highlights the importance of understanding how these models arrive at their outputs, regardless of whether we label it "thinking" or not. They emphasize the need for greater transparency and interpretability in LLMs.
One commenter draws a parallel to human cognition, suggesting that the distinction between explicit and implicit processing might be relevant to understanding LLMs. They propose that while LLMs don't have conscious thought, their complex internal processing could be analogous to the unconscious processing that occurs in the human brain.
The concept of "chain-of-thought prompting" is mentioned, highlighting a technique where the model is prompted to explicitly lay out its reasoning steps. This is contrasted with the "hidden thinking" discussed in the paper, where the internal reasoning process is not directly observable.
Finally, some comments express skepticism about the novelty of the "hidden thinking" concept, suggesting that similar observations have been made previously in the field of machine learning. They question whether the paper presents genuinely new insights or simply repackages existing ideas.
Overall, the comments reflect a mixture of fascination and skepticism regarding the idea of "hidden thinking" in LLMs. While acknowledging the impressive capabilities of these models, commenters emphasize the need for a more nuanced understanding of their internal processes and caution against anthropomorphizing their behavior. The discussion highlights ongoing debates within the AI community about interpretability, efficiency, and the very nature of intelligence in artificial systems.