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Donate arxiv logo > cs > arXiv:2505.17989 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2505.17989 (cs) [Submitted on 23 May 2025 (v1), last revised 26 May 2025 (this version, v2)] Title:Outcome-based Reinforcement Learning to Predict the Future Authors:Benjamin Turtel, Danny Franklin, Kris Skotheim, Luke Hewitt, Philipp Schoenegger View a PDF of the paper titled Outcome-based Reinforcement Learning to Predict the Future, by Benjamin Turtel and 4 other authors View PDF HTML (experimental) Abstract:Reinforcement learning with verifiable rewards (RLVR) has boosted math and coding in large language models, yet there has been little effort to extend RLVR into messier, real-world domains like forecasting. One sticking point is that outcome-based reinforcement learning for forecasting must learn from binary, delayed, and noisy rewards, a regime where standard fine-tuning is brittle. We show that outcome-only online RL on a 14B model can match frontier-scale accuracy and surpass it in calibration and hypothetical prediction market betting by adapting two leading algorithms, Group-Relative Policy Optimisation (GRPO) and ReMax, to the forecasting setting. Our adaptations remove per-question variance scaling in GRPO, apply baseline-subtracted advantages in ReMax, hydrate training with 100k temporally consistent synthetic questions, and introduce lightweight guard-rails that penalise gibberish, non-English responses and missing rationales, enabling a single stable pass over 110k events. Scaling ReMax to 110k questions and ensembling seven predictions yields a 14B model that matches frontier baseline o1 on accuracy on our holdout set (Brier = 0.193, p = 0.23) while beating it in calibration (ECE = 0.042, p < 0.001). A simple trading rule turns this calibration edge into \$127 of hypothetical profit versus \$92 for o1 (p = 0.037). This demonstrates that refined RLVR methods can convert small-scale LLMs into potentially economically valuable forecasting tools, with implications for scaling this to larger models. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2505.17989 [cs.LG] (or arXiv:2505.17989v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2505.17989 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Philipp Schoenegger [view email] [v1] Fri, 23 May 2025 14:56:07 UTC (427 KB) [v2] Mon, 26 May 2025 15:34:33 UTC (427 KB) Full-text links: Access Paper: View a PDF of the paper titled Outcome-based Reinforcement Learning to Predict the Future, by Benjamin Turtel and 4 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.LG < prev | next > new | recent | 2025-05 Change to browse by: cs cs.AI References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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