Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use
Memory enables language model assistants to maintain continuity across sessions and generate personalized responses, but knowing more about a user does not necessarily produce responses they prefer. Existing evaluations assess memory capabilities without directly establishing whether memory use benefits the user. To study this gap, we collect approximately 1,000 pairwise judgments from 87 participants using their own ChatGPT queries and personal memories. We find that memory-conditioned responses are not consistently preferred over responses generated without memory and in some cases, users strongly prefer the latter. Automatic evaluators also show limited agreement with users and favor m...
| Authors | Yijiang River Dong, Zexue He, Cheng Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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