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Do Rollups Self-Organize? Multi-Agent Reinforcement Learning in the EIP-4844 Blob Fee Market

Alessio Brini · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.15256 · cited by 0 works (OpenAlex) · open-access PDF

Korean

EIP-4844는 Ethereum 롤업이 데이터에 대한 별도의 요금 차원을 제공했기 때문에 블록당 3 개의 "블로브" 요금을 목표로하는 다수 업데이트 규칙에 의해 지배됩니다.이 목표는 블로브 기초 요금이 내재적 인 블로브 기초 요금이있는 블로브 요금 시장에 대한 캘리브레이드 된 다중 에이전트 강화 학습 환경을 제안 한 후, 우리는 블로브 기금 시장에 대한 캘리브레이드 된 분석 요금 최적화 환경을 확인 한 후, 블로브 기초 요금이 내재적 인 EIP-4844에 의해 설정 된 다수 형식의 롤업의 집계 결정에 대한 규칙에 따라 달라집니다. 다섯 개의 아날로그 롤업에 대한 합계 결정에

Machine translation (Meta M2M100); the English original follows.

Abstract

EIP-4844 gave Ethereum rollups a separate fee dimension for data, governed by a multiplicative update rule that targets three ``blobs'' per block. Whether this target is reached depends on how many independent, self-interested rollups choose to post data, a question that closed-form models address only for a single agent under exogenous prices or in a static equilibrium. We propose a calibrated multi-agent reinforcement learning environment for the blob fee market in which the blob base fee is endogenous, set by the EIP-4844 rule on the aggregated decisions of heterogeneous rollups fit to post-Dencun Ethereum data. After confirming that the environment reproduces the known single-agent analytical optimum, we show that independent learners, with no convergence guarantee, still settle near the target without coordination ($3.32\pm0.01$ blobs per block over five seeds). Over a fiftyfold demand range ($10\times$ to $500\times$), supply stays at the target from $10\times$ to $200\times$ and rises above it at higher demand, where the fee can no longer hold it. Posting frequency increases with rollup size, an emergent analog of the size threshold predicted by prior theory. A costless layer-1 calldata outside option pushes the fee down by roughly an order of magnitude while aggregate supply stays at the target, so the fee rule acts as a negative-feedback controller on decentralized supply.

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Topics: Multiplicative function, Dimension (graph theory), Set (abstract data type), Range (aeronautics), Aggregate (composite), Reinforcement learning

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