Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enables large language models to effectively propagate information across the entire sequence. Therefore, leveraging reward models (RMs) to automatically and reliably evaluate memory quality is critical. In this work, we introduce MemoryRewardBench, the first benchmark to systematically study the ability of RMs to evaluat...
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---
name: memoryrewardbench-benchmarking-reward-models-for
title: "MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.11969"
keywords: [Benchmark, Memory]
description: "Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enables large language models to effectively propagate information across the entire sequence. Therefore, leveraging reward models (RMs) to automatically and reliably evaluate memory quality is critical. In this work, we introduce MemoryRewardBench, the first benchmark to systematically study the ability of RMs to evaluate l..."
---
## Problem
MemoryRewardBench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.
## Key Approach
The paper introduces a novel framework, methodology, or benchmark for memoryrewardbench. The core contributions include:
1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. Generalizable principles applicable across domains
## When to Use
Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities
## When NOT to Use
- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.11969
- Full PDF: https://arxiv.org/pdf/2601.11969
- HTML Version: https://arxiv.org/html/2601.11969
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
