The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain

arXiv:2510.14642 · cs.GT, cs.AI, cs.DC · Submitted 2026-08-20 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain".

Jane: The paper was written by the authors from.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Summary: Tom: Okay, so we've established that "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain" is all about modeling competition in transaction order. Now, the paper gets into the nuts and bolts of how they approached this challenge.

Jane: If you look at their summary, what really jumps out is that they aren't just suggesting a single greedy strategy; they're looking at how complex interactions between multiple bidders create a dynamic environment.

Lu: And this is where the reinforcement learning comes in so heavily. They’ve structured it as an agent needing to learn optimal bidding strategies against other unknown, self-interested agents—that's the core difficulty of any real-world market game.

Meng: The paper seems to model different types of MEV, like liquidations or sandwich attacks. When they summarize those methods, are they providing enough technical detail for someone who wants to actually build a competing bot?

Jane: They do cover the methodology quite well, Meng. They outline how various transaction bundles can be constructed and then optimized using the RL framework to determine the most profitable sequence of execution.

Tom: So, it's not just "here’s a profit," it's "here’s the optimal *sequence* of actions to guarantee maximum profit." That distinction is huge for anyone looking at building DeFi tools.

Lalam: What I find fascinating in the summary is how deeply they integrate game theory principles with AI agents. It elevates MEV from a simple technical vulnerability into a profound problem of economic coordination and self-interest.

Lu: The implication here, beyond just profit, is that this framework could be used to model *any* competitive market where execution order matters—it's not limited to crypto anymore.

Jane: Exactly. Understanding the mechanism of value extraction in DeFi gives us a template for understanding value extraction in other regulated or quasi-regulated industries, too.

Tom: It really makes you think about the structure of trust itself within these digital economies. Before we talk about improvements, I want to make sure everyone grasps how sophisticated this modeling is.

Meng: Speaking of sophistication, I wonder if the simulations they ran were robust enough to handle network congestion or sudden protocol changes? That's usually where these models break down in practice.

Lalam: Thinking about the summary's implications for culture, if MEV becomes a highly predictable game, it might lead to a massive concentration of wealth among those who control the best algorithms.

Lu: If we can model the bidding games this precisely, we should be able to build predictive risk models that warn users when their transactions are likely to be intercepted or optimized away by sophisticated actors.

Jane: And for the average user, understanding that this value exists—that there's a 'bidding game' happening—is the first step toward demanding better protocol transparency.

Tom: Okay, so we’ve seen how they model the game and what it entails. Next up, I think we need to talk about how these authors think we can make this even better.

Improvements: Jane: We're moving into the improvements suggested by "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain," and what's striking is that the authors aren't just suggesting tweaking their current model; they are pointing toward systemic changes.

Tom: They suggest improving the approach, which implies that while their current model is good, it's not perfect or complete. What kind of improvements are they focusing on?

Lu: I noticed they talk about integrating more advanced types of state representation into the RL agent. It suggests moving beyond simple transaction data to incorporate broader market signals and external economic indicators for better decision-making.

Meng: From an engineering viewpoint, adding more state variables sounds computationally heavy, Lu. Are they suggesting a massive increase in the complexity of the training environment, which could slow down real-time execution?

Jane: That's a valid concern, Meng. But the goal seems to be making the model *smarter*, not just bigger. They want it to handle uncertainty and adversarial behavior more gracefully than current models do.

Lalam: I appreciate that they are pushing for improvements because stagnation is what allowed this problem to fester in the first place. The suggestions for improvement point toward a future where economic systems are proactively defended against exploitation, which is a huge cultural leap forward.

Tom: So, it’s less about just extracting value and more about building mechanisms to *guard* against unwanted extraction using these advanced AI techniques?

Lu: Precisely. They're advocating for incorporating decentralized or transparent mechanisms into the bidding process itself—making the rules of the game visible to everyone, not just the insiders.

Jane: And another area they touch on is improving how agents handle risk and uncertainty, suggesting methods that are more robust when faced with unexpected network conditions or sudden shifts in liquidity.

Meng: Robustness is key for me. If these proposed improvements require integrating off-chain data feeds that aren't perfectly reliable, the entire system

Paper discussion segment 3: Tom: So we've talked about how much MEV is and how the bidding works; now we gotta focus on what improvements this research actually suggests for the system and what that means for developers.

Jane: That’s right, Tom; basically, they’re showing us a smarter way to approach these auctions than just guessing or using simple scripts.

Lu: What I find really exciting is how they frame this as a dynamic optimization problem; it's not just about the current state, it's about predicting the optimal sequence of actions over time.

Meng: But Lu, when you say "optimal sequence," are we talking about something that needs massive computational power running constantly, or can this actually be implemented on existing infrastructure?

Jane: It’s less about raw computing power and more about making the bidding strategy adaptive—it learns from every single failed or successful bid.

Tom: Exactly, Jane; they aren't just finding the highest bid; they're finding the *best* bid, which is a huge shift in thinking for anyone involved in blockchain finance.

Lu: The implication here is that autonomous agents can now play these bidding games with a level of strategic depth previously thought impossible outside of highly specialized game theory simulations.

Meng: If we could automate that strategic depth, it would fundamentally change the barrier to entry; smaller players wouldn't be wiped out by simple arbitrage bots anymore.

Jane: That’s right, Meng; it brings a level of sophistication to decentralized finance that makes these markets feel much more like real-world competitive environments.

Tom: So, instead of just being a guessing game, the system is actually teaching itself the optimal playbook for value extraction.

Lalam: When you think about the cultural impact, this moves DeFi from being merely speculative to becoming truly algorithmic and highly optimized; it elevates the entire culture of decentralized finance.

Lu: It suggests a future where smart contracts are not just passive rules but actively participating, learning entities that improve their own economic model.

Meng: From an engineering standpoint, this means we might finally move toward more reliable and predictable yield generation mechanisms if these RL agents can stabilize the extraction process.

Jane: So, to wrap up this section simply: they're giving us the roadmap for making MEV extraction less of a gamble and more of a calculated business strategy.

Lalam: Considering that, the next frontier must be integrating these sophisticated bidding strategies with real-world asset management systems to unlock truly global financial efficiency.

Conclusion: Tom: So, we've spent some time today really looking at how sophisticated AI can be applied to what’s essentially a high-stakes auction environment right on Polygon.

Jane: And what's fascinating is that this paper, "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain," shows that thinking about transaction selection as a game changes the whole way we view DeFi security and efficiency.

Meng: You mentioned how they used reinforcement learning—that’s the part that really struck me; it implies that the system isn't just running one optimal path, but constantly predicting opponent behavior to maximize gain.

Lu: Exactly! It suggests we're moving past simple consensus mechanisms and into an era where decentralized ledgers are modeled as complex, multi-agent competitive systems, which is wildly exciting from a modeling standpoint.

Lalam: Considering the sheer level of strategic prediction involved, this research points toward a future where financial infrastructure isn't just transparently recorded, but actively optimized by autonomous intelligence to improve resource distribution across the entire network.

Tom: That brings up the massive implication that if AI can predict these opportunities so well, it could drastically change who has access to the best trading strategies in DeFi.

Jane: It makes you think about centralization risks again; even if the ledger is decentralized, the *intelligence* required to navigate it seems incredibly concentrated right now.

Meng: From an engineering viewpoint, I wonder about the latency requirements for these bidding games—if you're doing real-time reinforcement learning on a live blockchain feed, what kind of computational overhead are we talking about?

Lu: The throughput demands must be insane, though; the models need to process not just block data, but entire trajectories of potential state changes in milliseconds.

Lalam: But even with those technical challenges, the cultural shift here is that it forces us to build AI systems that are robustly decentralized themselves, ensuring no single entity controls the core optimization logic.

Tom: It’s a really powerful look at how these financial markets aren't just about money moving; they're about information asymmetry and predictive power.

Jane: And honestly, understanding this complexity is going to be vital for anyone building the next generation of smart contracts, isn't it?

Lu: Absolutely; we need AI that can not only execute trades but also model the ethical boundaries of those trades within a decentralized framework.

Meng: Right, so if we take the practical implication away, any enterprise adopting this tech needs to build a simulation environment first before ever touching a live blockchain.

Lalam: And ultimately, the success of "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain" shows that the next frontier in finance is genuinely intelligent orchestration across distributed networks.

Tom: Wow, what an incredibly deep dive into advanced AI and blockchain mechanics; thanks to all of you for breaking this down for us.

Jane: We really appreciate your insights today, and we'll be ready when you are to look at the next groundbreaking paper!

cs.GT, cs.AI, cs.DC

Submitted: 2026-08-20

Updated: 2026-08-21

Code: https://github.com/ethereum/wiki

Importance score: 78/100

The gist: The paper, "The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain," details a novel framework for maximizing profit by strategically capturing Maximal Extractable Value

Key concepts

MEV Extraction
Maximum Extractable Value refers to the value that can be extracted from blockchain transactions by reordering or including them. The paper models this extraction process as a competitive 'bidding game' among self-interested agents.
Reinforcement Learning (RL)
RL is an AI framework used in the paper to teach an agent optimal bidding strategies. Instead of following fixed rules, the agent learns by interacting with a dynamic environment and maximizing its cumulative reward.
Bidding Games
This concept treats transaction order as a competitive market where multiple agents (bidders) compete for profitable execution slots. The goal is to predict the optimal sequence of actions to guarantee maximum profit.

Terminology

Summary

The paper, The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain, details a novel framework for maximizing profit by strategically capturing Maximal Extractable Value (MEV) within the decentralized environment of the Polygon blockchain. The core contribution revolves around modeling MEV extraction not as a simple arbitrage opportunity, but as a complex, multi-agent bidding game solvable through advanced Reinforcement Learning (RL) techniques.

The research first establishes a comprehensive understanding of MEV, referencing existing academic work that categorizes and analyzes the concept—including discussions on Maximal extractable value: Current understanding, categorization, and open research questions [8]. The authors situate this problem within the context of blockchain mechanics, noting that capturing MEV is crucial for profitability in decentralized finance (DeFi) ecosystems.

The methodology centers on adapting RL to solve the dynamic decision-making process inherent in block construction and transaction ordering. The paper draws parallels between this process and established areas of machine learning, such as Real-time bidding with multi-agent reinforcement learning in multi-channel display advertising [5] and [12], suggesting that the optimal strategy for an MEV extractor requires predicting market movements and competing bids in real time.

Specifically applied to Polygon, the framework treats the interaction between potential MEV extractors (searchers) as a competitive game. The RL agent is tasked with learning an optimal bidding policy—a sequence of actions that maximizes expected utility while accounting for transaction fees and network congestion. This differs from simpler approaches by incorporating strategic depth, moving beyond basic arbitrage to tackle complex scenarios like liquidations or sandwich attacks.

The paper’s analysis utilizes data derived from the Polygon blockchain, potentially drawing insights related to DeFi Lending Platform Liquidity Risk: The Example of Folks Finance [9] and analyzing specific institutional data sets, such as the Loan Portfolio Dataset From MakerDAO Blockchain Project [5]. By framing the problem as a game, the model learns to predict which transaction sequence will yield the highest value while minimizing the risk of being outbid or blocked by other participants.

The implementation details involve training an RL agent on simulated blockchain environments. The agent learns to optimize its bid size and timing based on observed market depth, pending transactions in the mempool, and predicted block inclusion probabilities. The authors argue that this RL approach provides a significant advantage over heuristic bidding strategies because it can model the non-linear interactions between multiple agents vying for limited block space.

In conclusion, the paper demonstrates that by employing sophisticated RL models to simulate and solve the competitive bidding game of MEV extraction on Polygon, participants can develop highly optimized, adaptive strategies for capturing value that surpasses what is achievable through static or reactive market analysis. The work contributes a practical, advanced methodology for navigating the complex economic landscape of modern decentralized ledgers.

Improvements for AI systems

Given the highly technical and specialized nature of this bibliography, which bridges advanced Game Theory, Multi-Agent Reinforcement Learning (MARL), and complex Financial Engineering within decentralized ledger technology (DLT), the primary area for AI improvement lies in creating Predictive, Adversarial Optimization Engines.

The current state-of-the-art research often treats MEV extraction and ad bidding as separate problems. The critical improvement is building a unified framework that models the entire blockchain transaction lifecycle as a dynamic, multi-agent competitive game.

Here are the specific improvements and the resulting capabilities of the enhanced AI system:

(Focusing on [6], [7], [8], [21])

The Improvement: We must move beyond reactive MEV detection (which only identifies value after it has occurred) to proactive, predictive modeling of block construction and transaction ordering. This requires integrating advanced graph representation learning with deep reinforcement learning.

Technical Implementation:

  1. Graph Neural Networks (GNNs) for Dependency Mapping: Use GNNs to model the entire mempool as a dynamic, weighted dependency graph. Nodes represent transactions, and edges represent potential dependencies (e.g., Tx A must execute before Tx B). The weights must incorporate gas costs, smart contract complexity, and predicted execution timing.

  2. Multi-Objective Reinforcement Learning (MORL): Implement an agent trained not just to maximize immediate profit (the current MEV model), but to optimize a weighted combination of multiple objectives: Maximize(Profit) + lambda 1 (Speed) - lambda 2 (Risk).

  3. Adversarial Simulation Layer: The system must run continuous Monte Carlo simulations, allowing the agent to predict how competing bots (other agents) will react to its proposed transaction order, thereby stress-testing the optimal execution path before submission.

What the Improved System Can Do:

  • Pre-emptive Arbitrage: Predict and execute optimal arbitrage/liquidation strategies before the transactions are visible or even fully formed in the mempool, granting a significant time advantage over current front-running methods.

  • Optimal Transaction Bundling: Automatically bundle multiple dependent transactions (e.g., swapping assets, staking collateral, and liquidating a loan) into a single atomic transaction set that maximizes the overall yield while minimizing gas costs and execution failure risk.

(Focusing on [4], [7], [16], [17])

(Focusing on [5], [11], [12] & applied to DeFi)

Sources

Related papers