KellyBench: A Benchmark for Long-Horizon Sequential Decision Making

📰 ArXiv cs.AI

Learn how KellyBench evaluates sequential decision-making in long-horizon environments, and how to apply it to sports betting markets

advanced Published 1 May 2026
Action Steps
  1. Configure a sequential decision-making model using KellyBench environment
  2. Train the model on historical sports betting data
  3. Evaluate the model's performance on a simulated 2023-24 English Premier League season
  4. Compare the model's results with baseline models
  5. Apply the model to real-world sports betting markets
Who Needs to Know This

AI researchers and engineers working on sequential decision-making and long-horizon planning can benefit from KellyBench, as it provides a realistic environment for evaluating their models

Key Insight

💡 KellyBench provides a realistic environment for evaluating sequential decision-making models in long-horizon, non-stationary environments

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🚀 Introducing KellyBench: a benchmark for long-horizon sequential decision making in sports betting markets! 🏈💰

Key Takeaways

Learn how KellyBench evaluates sequential decision-making in long-horizon environments, and how to apply it to sports betting markets

Full Article

Title: KellyBench: A Benchmark for Long-Horizon Sequential Decision Making

Abstract:
arXiv:2604.27865v1 Announce Type: new Abstract: Language models are saturating benchmarks for procedural tasks with narrow objectives. But they are increasingly being deployed in long-horizon, non-stationary environments with open-ended goals. In this paper we introduce KellyBench, an environment for evaluating sequential decision-making in sports betting markets. Agents are placed in a sequential simulation of the 2023-24 English Premier League season and tasked with maximising their long-term
Read full paper → ← Back to Reads

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