Researchers Develop Memory-Augmented Training to Improve LLM Performance on Multi-Turn Conversations
A new study shows that large language models experience up to 65% accuracy drops when task-critical information arrives across multiple conversation turns, even when the full context is available. Researchers developed a memory-augmented reinforcement learning approach with a scalable sharding pipeline that converts single-turn datasets into multi-turn fragmented-information episodes for training. The findings suggest that training models to compress and maintain rolling memory improves both multi-turn reasoning and generalization to harder tasks, with potential applications for more robust conversational AI systems.
Researchers at arXiv have identified a significant performance degradation in large language models when processing information revealed incrementally across conversation turns, termed the "Lost in Conversation" problem. Despite having access to full context, models showed accuracy drops of up to 65%. To address this, the team introduced a memory-augmented reinforcement learning approach that trains models to maintain a compact rolling memory rather than attending to an expanding conversation history. A key innovation is a low-cost sharding pipeline that automatically converts single-turn question-answering datasets (specifically GSM8K) into multi-turn episodes with fragmented information, eliminating the need for expensive manual annotation. Models trained with this approach demonstrated improved multi-turn accuracy and zero-shot generalization to harder mathematical reasoning tasks and out-of-domain long-context QA. Notably, memory-trained models outperformed full-history baselines even when given complete context at test time, suggesting that learning to compress information induces more robust incremental reasoning capabilities.
What's missing
The study does not discuss computational costs or inference latency comparisons between memory-augmented and full-history approaches. Additionally, the paper does not address potential limitations of the rolling memory approach for tasks requiring access to information from early conversation turns, or provide analysis of failure modes.
What different sources said
- arXiv cs.CLCenter
Multi-Turn Reasoning When Context Arrives in Pieces: Scalable Sharding and Memory-Augmented RL
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