LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on the ability to effectively leverage extensive, long user historical data due to its inherent noise and length of such data. Existing pretrained LLMs may generate summaries that are concise but lack the necessary context for downstream tasks, hindering their utility in personalization systems. To address these challenges, we introduce Reinforcement Learning from Prediction Feedback (RLPF). RLPF fine-tunes LLMs to generate concise, human-readable user summaries that are optimized for downstream task performance. By maximizing the usefulness of the generated summaries, RLPF effectively distills extensive user history data while preserving essential information for downstream tasks. Our empirical evaluation demonstrates significant improvements in both extrinsic downstream task utility and intrinsic summary quality, surpassing baseline methods by up to 22% on downstream task performance and achieving an up to 84.59% win rate on Factuality, Abstractiveness, and Readability. RLPF also achieves a remarkable 74% reduction in context length while improving performance on 16 out of 19 unseen tasks and/or datasets, showcasing its generalizability. This approach offers a promising solution for enhancing LLM personalization by effectively transforming long, noisy user histories into informative and human-readable representations.

In this paper we consider the capacity of a binary associative net (Willshaw, Buneman, & Longuet-Higgins, 1969; Willshaw, 1971; Buckingham, 1991) containing these features. While the associative net is a very simple model of associative memory, its behaviour as a storage device is not trivial and yet it is tractable to theoretical analysis.

In this paper we consider the capacity of a binary associative net (Willshaw, Buneman, & Longuet-Higgins, 1969; Willshaw, 1971; Buckingham, 1991) containing these features. While the associative net is a very simple model of associative memory, its behaviour as a storage device is not trivial and yet it is tractable to theoretical analysis.

Bruce Graham and David Willshaw Centre for Cognitive Science, University of Edinburgh 2 Buccleuch Place, Edinburgh, EH8 9LW, UK Email: bruce@cns.ed.ac.uk&david@cns.ed.ac.uk Abstract We have determined the capacity and information efficiency of an associative net configured in a brain-like way with partial connectivity andnoisy input cues. Recall theory was used to calculate the capacity when pattern recall is achieved using a winners-takeall strategy.Transforming the dendritic sum according to input activity and unit usage can greatly increase the capacity of the associative net under these conditions. This corresponds to the level of connectivity commonly seen in the brain and invites speculation that the brain is connected in the most information efficient way. 1 INTRODUCTION Standard network associative memories become more plausible as models of associative memoryin the brain if they incorporate (1) partial connectivity, (2) sparse activity and (3) recall from noisy cues. In this paper we consider the capacity of a binary associative net (Willshaw, Buneman, & Longuet-Higgins, 1969; Willshaw, 1971; Buckingham, 1991) containing these features. While the associative net is a very simple model of associative memory, its behaviour as a storage device is not trivial and yet it is tractable to theoretical analysis. We are able to calculate 514 BruceGraham, David Willshaw the capacity of the net in different configurations and with different pattern recall strategies.