MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents (2506.15841v1)

Abstract: Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to unbounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. This state integrates prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. To support training in more realistic and compositional settings, we propose a simple yet effective and scalable approach to constructing multi-turn environments by composing existing datasets into arbitrarily complex task sequences. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon interactive agents, where both efficiency and performance are optimized.

• The paper presents MEM1, an architecture that synergizes memory consolidation with integrated reasoning to overcome unbounded memory growth in LLMs.
• The experimental results show MEM1-7B achieving a 3.5-fold performance boost while reducing memory usage by 3.7 times compared to baselines.
• The methodology employs reinforcement learning with a compact shared state to discard redundant data, ensuring efficient and scalable multi-turn interactions.

MEM1: Memory-Efficient Mechanism via Learning 1-step Integrated Reasoning and Consolidation

The paper "MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents" by Zijian Zhou et al. presents a novel architecture for LLMs that aims to address the intrinsic challenges of long-horizon, multi-turn interaction tasks. In conventional LLM systems, maintaining a complete interaction history leads to unbounded memory growth and computational inefficiencies, which inhibit the model's reasoning capabilities, particularly with out-of-distribution input lengths. MEM1 proposes an innovative framework that combines reinforced learning with efficient memory management, allowing constant memory operations across multiple turns by integrating reasoning and memory consolidation.

Methodology

The core of MEM1 is its architecture, which utilizes a compact and shared internal state mechanism to consolidate memory and facilitate reasoning. The proposed framework strategically discards irrelevant or redundant information while updating its internal state with new observations. This methodology signifies a significant reduction in memory usage, promoting efficient multi-turn interactions without the progressive accumulation of context that plagues existing systems.

The MEM1 framework is tested in expansive multi-turn environments formulated from existing datasets, which pose complex task sequences and offer realistic and compositional settings for evaluation. These environments include internal retrieval QA, open-domain web QA, and multi-turn web shopping tasks. These scenarios rigorously assess MEM1's capacity to reformulate its approach based on previous interactions, validating its scalability and efficiency.

Experimental Results

The experimental results showcase MEM1's performance improvements over baseline models. Notably, in a 16-objective multi-hop QA task, MEM1-7B outperformed Qwen2.5-14B-Instruct, providing a 3.5-fold increase in task performance while reducing memory usage by 3.7 times. This illustrates MEM1’s prominence in both efficiency and task effectiveness in long-horizon interactive tasks compared to its predecessors.

Implications and Future Directions

The development of MEM1 has several implications. Practically, it provides a solution to interaction-based LLM tasks where memory scalability is a persistent issue. With continued improvements, MEM1 could serve as a model for deploying LLMs in environments like real-time customer service or continuous monitoring systems, where memory resource optimization is critical. Theoretically, MEM1 advances our understanding of the synergetic relationship between memory and reasoning in LLMs, illustrating the importance of integrated state consolidation in enhancing performance across distributed tasks.

Future developments might explore MEM1's adaptability to more diverse task environments, including those with uncertain or dynamic information availability, and examine its integration with other memory-efficient strategies or architectures. Additionally, the paper opens avenues for exploring how memory consolidation can further enhance models' interpretability and decision-making processes.

In summary, MEM1 presents a compelling approach to improving the scalability and performance of LLMs in multi-turn tasks by optimally balancing memory and reasoning integration through reinforcement learning.