InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
#InftyThink+ #Reinforcement Learning #Chain-of-Thought #Large Reasoning Models #Iterative Reasoning #arXiv #Computational Efficiency
📌 Key Takeaways
- InftyThink+ addresses the quadratic cost and context limits of traditional chain-of-thought reasoning.
- The framework uses reinforcement learning to determine when to summarize and what data to keep.
- It solves the 'lost-in-the-middle' effect where models forget information in the center of a long context.
- The method moves away from rigid heuristics toward a more adaptive, iterative reasoning process.
📖 Full Retelling
Researchers recently introduced InftyThink+, a novel framework designed to achieve infinite-horizon reasoning through reinforcement learning, according to a paper published on the arXiv preprint server on February 11, 2025. This breakthrough aims to overcome the significant computational and structural barriers currently hindering large reasoning models (LRMs) when they engage in extended chain-of-thought (CoT) processes. By integrating reinforcement learning, the team seeks to solve the 'lost-in-the-middle' effect and the quadratic cost scaling that typically occur during complex, multi-step problem-solving.
While traditional large reasoning models excel by scaling inference-time thought processes, they are frequently limited by the finite context windows of their underlying architectures. As the chain-of-thought grows longer, the computational expense increases quadratically, making deep reasoning tasks prohibitively expensive and prone to errors. Existing iterative reasoning techniques have attempted to mitigate these issues through periodic summarization; however, these methods often rely on rigid, pre-defined heuristics or supervised learning datasets that do not adapt to the specific nuances of a given problem.
The InftyThink+ approach distinguishes itself by automating the decision-making process behind information management during reasoning. Instead of following fixed rules, the model utilizes reinforcement learning to dynamically determine the optimal moments to summarize intermediate thoughts and identify which specific information is critical to preserve for future steps. This eliminates the need for manual tuning and allows the model to maintain a high level of reasoning accuracy over an effectively infinite horizon.
By optimizing the transition from raw thoughts to summarized insights, InftyThink+ effectively bypasses the context length limits that previously constrained AI performance on highly complex tasks. This architectural shift marks a significant move toward more efficient and scalable artificial intelligence, potentially enabling models to handle massive datasets and intricate logical sequences that were once considered computationally impossible.
🏷️ Themes
Artificial Intelligence, Machine Learning, Technology
📚 Related People & Topics
Reasoning model
Language models designed for reasoning tasks
A reasoning model, also known as reasoning language models (RLMs) or large reasoning models (LRMs), is a type of large language model (LLM) that has been specifically trained to solve complex tasks requiring multiple steps of logical reasoning. These models demonstrate superior performance on logic,...
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
🔗 Entity Intersection Graph
Connections for Reasoning model:
- 🌐 Chain of thought (2 shared articles)
- 🌐 Reinforcement learning (2 shared articles)
- 🌐 LRM (1 shared articles)
- 🌐 Vector field (1 shared articles)
- 🌐 Resource exhaustion attack (1 shared articles)
- 🌐 Adversarial machine learning (1 shared articles)
- 🌐 Large language model (1 shared articles)
- 🌐 Artificial intelligence (1 shared articles)
- 🌐 Machine learning (1 shared articles)
📄 Original Source Content
arXiv:2602.06960v1 Announce Type: cross Abstract: Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, context length limits, and degraded reasoning due to lost-in-the-middle effects. Iterative reasoning mitigates these issues by periodically summarizing intermediate thoughts, yet existing methods rely on supervised learning or fixed heuristics and fail to optimize when to summarize, what to preserve, and ho