Concentrated Liquidity Provision: a Reinforcement Learning Perspective

• URL: http://arxiv.org/abs/2608.19389v1
• Date: Wed, 19 Aug 2026 19:08:39 GMT
• Title: Concentrated Liquidity Provision: a Reinforcement Learning Perspective
• Abstract summary: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi)<n>We formulate dynamic liquidity provision as an impulse control problem and use reinforcement learning (RL) to solve it.<n>We show that policies exhibit rich state-dependent behaviour, allocating liquidity according to mispricing, rebalancing costs, uncertainty, inventory exposure, and heterogeneous risk preferences.
• Score: 6.803908016694179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, such as UniswapV3, are now a well-established design. In these markets, liquidity providers (LPs) face a sequential decision problem: they must decide when to rebalance their positions and which price ranges to allocate capital to as market conditions evolve. We formulate dynamic liquidity provision as a stochastic impulse control problem and use reinforcement learning (RL) to solve it, focusing on providing interpretable solutions. We show that learned policies exhibit rich state-dependent behaviour, allocating liquidity according to mispricing, rebalancing costs, uncertainty, inventory exposure, and heterogeneous risk preferences. These behaviours help compress the left tail of the Profit and Loss (PnL) distribution and avoid catastrophic outcomes under high uncertainty. Finally, we benchmark the RL agents against baseline and sophisticated agents from the AMM microstructure literature and analyse their performance.

Related papers

• From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention [46.90931293070464]
  We propose an autonomous agent that perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations.<n>We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles.
  arXiv  Detail & Related papers  (2026-04-16T03:22:05Z)
• LiquiLM: Bridging the Semantic Gap in Liquidity Flaw Audit via DCN and LLMs [7.012454472956686]
  We propose the LiquiLM framework, which integrates Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN)<n>By establishing a dynamic interaction between liquidity-critical contracts and flaw descriptions, the framework effectively bridges the semantic gap between underlying code implementations and high-level liquidity intents.<n>LiquiLM successfully identifies 238 high-risk contracts and assists in discovering 10 vulnerabilities that have received CVE certification.
  arXiv  Detail & Related papers  (2026-04-04T20:49:09Z)
• Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets [57.179679246370114]
  In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices.<n>During deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.<n>Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties.<n>We develop a novel class of elliptic uncertainty sets, enabling efficient and tractable robust policy evaluation.
  arXiv  Detail & Related papers  (2025-10-22T18:22:25Z)
• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [62.388554963415906]
  In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data.<n>These conflicts are especially problematic in real-world investment services, where a model's inherent biases can misalign with institutional objectives.<n>We propose an experimental framework to investigate emergent behaviors in such conflict scenarios, offering a quantitative analysis of bias in investment analysis.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation [55.2788567621326]
  We introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation.<n>By curating financial news headlines, FIN-FORCE supports LLM based forward counterfactual generation.<n>This paves the way for scalable and automated solutions for exploring and anticipating future market developments.
  arXiv  Detail & Related papers  (2025-05-26T02:41:50Z)
• Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning [0.3376269351435395]
  This paper applies deep reinforcement learning (DRL) to optimize liquidity provision in a DeFi protocol.<n>By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants.
  arXiv  Detail & Related papers  (2025-01-13T17:27:11Z)
• ZeroSwap: Data-driven Optimal Market Making in DeFi [23.671367118750872]
  Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. We propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset.
  arXiv  Detail & Related papers  (2023-10-13T21:28:19Z)
• Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning [19.916721360624997]
  Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi) This paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust price ranges. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio.
  arXiv  Detail & Related papers  (2023-09-18T20:10:28Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• UAMM: Price-oracle based Automated Market Maker [42.32743590150279]
  We propose a new approach known as UBET AMM, which calculates prices by considering external market prices and the impermanent loss of the liquidity pool. We demonstrate that our approach eliminates arbitrage opportunities when external market prices are efficient.
  arXiv  Detail & Related papers  (2023-08-11T20:17:22Z)
• Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
  Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards.
  arXiv  Detail & Related papers  (2023-02-01T17:21:40Z)
• Learning Strategies in Decentralized Matching Markets under Uncertain Preferences [91.3755431537592]
  We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space. We derive optimal strategies that maximize agents' expected payoffs.
  arXiv  Detail & Related papers  (2020-10-29T03:08:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.