MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
MILES introduces a modular instruction memory framework that dynamically expands step-wise memory to accumulate reusable experience across sequential problems. The system utilizes asymmetric pairs of sub-goal embeddings and sub-instructions, paired with learnable selection heads to optimize for final-answer correctness. A coarse-to-fine retrieval mechanism allows for memory expansion and supervision collection from confident samples, while refining reasoning for uncertain ones. MILES achieves su
Analysis
TL;DR
- MILES introduces a modular instruction memory framework that dynamically expands step-wise memory to accumulate reusable experience across sequential problems.
- The system utilizes asymmetric pairs of sub-goal embeddings and sub-instructions, paired with learnable selection heads to optimize for final-answer correctness.
- A coarse-to-fine retrieval mechanism allows for memory expansion and supervision collection from confident samples, while refining reasoning for uncertain ones.
- MILES achieves superior accuracy-efficiency trade-offs compared to prior methods that rely on rigid templates or heuristic selection.
- The approach addresses the limitation of isolated problem solving by enabling self-improving LLM reasoning under realistic test-time constraints with limited supervision.
Why It Matters
This research is critical for advancing test-time compute strategies, moving beyond static prompting to dynamic, experience-based reasoning that improves over time. For practitioners, it offers a viable path to enhance LLM reliability in sequential tasks without requiring extensive pre-training data or fixed action spaces. It bridges the gap between heuristic memory usage and learned selection policies, making self-improvement feasible in low-supervision environments.
Technical Details
- Modular Memory Units: The core architecture stores asymmetric pairs of sub-goal embeddings and sub-instructions, allowing for granular reuse of reasoning steps rather than whole-solution templates.
- Learnable Selection Heads: Each memory unit is associated with a selection head trained to predict correctness, replacing heuristic step-level selection with a learned policy optimized for final answer accuracy.
- Coarse-to-Fine Retrieval: The process involves a coarse stage for memory expansion and gathering supervision from confident samples, followed by a fine stage where learned heads rerank candidates to guide reasoning for uncertain samples.
- Incremental Adaptation: The framework is designed for test-time settings where memory expands incrementally, handling the challenge of limited supervision by leveraging confident samples for training selection policies.
Industry Insight
- Shift to Dynamic Memory: Organizations should prioritize developing dynamic memory systems that evolve with usage, rather than relying on static prompt libraries or one-off solutions.
- Efficiency in Test-Time Compute: Implementing coarse-to-fine retrieval mechanisms can significantly reduce computational overhead while maintaining or improving accuracy, a key factor for scalable AI deployment.
- Self-Improvement Pipelines: Integrating learnable selection policies into LLM workflows can create systems that autonomously improve their reasoning capabilities over time, reducing the need for frequent model retraining.
Disclaimer: The above content is generated by AI and is for reference only.