Impact of Thought Switching on LLM Performance

Researchers from Meta recently introduced COCONUT (Chain of Continuous Thought): a new reasoning approach that uses the last hidden state of the LLM as a representation of the reasoning state (termed “continuous thought”) COCONUT shifts LLMs from language-bound reasoning to a continuous latent space, unlocking advanced problem-solving efficiency and accuracy. 🤔 The problem: Traditional reasoning methods rely heavily on language-based reasoning chains, the familiar CoT and its variants. While effective for some tasks, these methods face inherent limitations: ➖ They prioritize fluency over reasoning, wasting computational effort by analyzing the intermediate logical steps in free text. ➖ When faced with complex tasks that demand planning or backtracking, CoT often struggles to map out all possibilities effectively. ➖ In general, these approaches mimic human communication patterns rather than the deeper cognitive processes involved in reasoning, 💡The solution: COCONUT introduces latent reasoning, a method where reasoning steps are represented as continuous states instead of explicit language tokens. This shift enables models to operate more effectively by: 🥥 Exploring multiple paths: encoding possibilities simultaneously, the model can evaluate alternatives, akin to a Breadth-First Search approach. 🥥 Backtracking: Latent reasoning supports revisiting earlier steps, crucial for tasks requiring complex planning. 🥥 Token efficiency: Continuous reasoning uses fewer tokens, reducing computational overhead. 🛠️ How COCONUT works: 🔹 Encoding reasoning: Continuous thoughts, derived from the model’s hidden states, represent the reasoning process. These states are looped back as input rather than being decoded into language. 🔹 Switching modes: The model alternates between two operational modes: 1. Language mode: Used for handling input questions and producing final answers. 2. Latent mode: Processes reasoning steps in the latent space, bypassing language generation. 📈 Performance: COCONUT outperforms CoT in various benchmarks, especially those involving planning-intensive tasks. More importantly, it does so with fewer tokens and while handling complex logical structures with ease. I find the main motivation really interesting: Authors claim that according to neuroimaging studies, reasoning in humans does not rely on the brain's language network, which primarily handles comprehension and communication. Instead, reasoning processes often involve distinct neural pathways optimized for logic and planning, independent of language structures. This insight is what motivated the development of 🥥COCONUT, aiming to separate the computational reasoning of LLMs from the constraints of language-based reasoning. Paper in comments.

Ever wondered if LLMs can think too much? New research shows that maintaining excessive reasoning history actually HARMS performance. Less can be more when it comes to AI reasoning! Researchers have introduced "Atom of Thoughts" (AOT), a framework that transforms complex reasoning in Large Language Models into a Markov process. Unlike traditional approaches that store and process entire reasoning chains, AOT only maintains information relevant to the current step—mimicking how humans naturally solve problems. When applied to gpt-4o-mini, AOT achieved an 80.6% F1 score on HotpotQA, surpassing specialized reasoning models like o3-mini (+3.4%) and DeepSeek-R1 (+10.6%). The key innovation is a two-phase mechanism: first decomposing questions into dependency-based directed acyclic graphs, then contracting subquestions into simplified, independent problems. Most impressively, AOT can serve as both a standalone framework and a plug-in enhancement for existing methods. By eliminating redundant historical information, it achieves higher performance while using significantly fewer computational resources—the test-time efficiency curves show the steepest performance-to-cost ratio among all compared methods. Could this Markov-style reasoning be the key to making LLMs more efficient and effective at complex problem-solving? What other cognitive science principles might still be waiting to revolutionize AI reasoning? Full paper link in comments. #AIResearch #MachineLearning #LLMs #ReasoningAI #ComputationalEfficiency

An interesting paper of building an even smarter DeepSeek R1 "Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs" and paperwithoutcode.com implementation of its Thought Switching Penalty (TIP) decoding strategy https://lnkd.in/gRxgByHt With emerging of OpenAI O1/O3 and nice open source from DeepSeek R1, test-time compute with thought process brought in many new research opportunities. Among many of them, underthinking of this RL approach appeared to improve the "thinking" efficiency for advanced reasoning. The research paper “On the Underthinking of o1-Like LLMs” addresses a critical inefficiency in advanced reasoning models like OpenAI’s o1/o3 and DeepSeek R1, by introducing the concept of “underthinking.” This phenomenon occurs when models frequently switch between reasoning strategies prematurely, leading to suboptimal problem-solving. The study contributes two key innovations: (1) an underthinking metric quantifying token efficiency in incorrect responses, and (2) a Thought Switching Penalty (TIP) decoding strategy. TIP works by dynamically adjusting the model’s logit distribution during generation, penalizing tokens associated with thought transitions (e.g., “alternatively”, “instead”) for a specified duration β after each thought begins. The penalty strength α is modulated based on the current thought’s depth, effectively encouraging models to explore promising reasoning paths more thoroughly. Unlike approaches requiring model retraining, TIP can be implemented via logit_bias parameters in standard LLM APIs. The paper’s empirical validation shows TIP improving accuracy across challenging datasets like MATH500 and AIME24, with optimal performance at α=3.0 and β=600 tokens. For researchers, this work provides both a framework for analyzing reasoning efficiency and a practical decoding-time solution for enhancing model performance. Future research directions include dynamic adjustment of TIP parameters and integration with reinforcement learning for adaptive depth control. In short, we know the naive RL just tried many things and picked these seemed working. Feedback, value prediction like AlphaGo, or any other heuristics could make it more efficient so a longer and more consistent reasoning could appear. LLM for general knowledge and thought has much more sparse search space than games like Go for both number of possibilities and long feedback time, so an efficient RL process is a tough task. What do you think about the next breakthrough for a smarter DeepSeek R1 competitor? Disclaimer: "PaperWithoutCode" project is for educational purposes only and it is not affiliated with paperswithcode[.]com . Please refer to this post for the motivation of the project https://lnkd.in/gv88kYb8 #AIForEducation #PaperWithoutCode #LLM #Agent #Reasoning #DeepSeek