4 papers
Foresight Learning for SEC Risk Prediction
Benjamin Turtel, Paul Wilczewski, Danny Franklin +1
Risk disclosures in SEC filings describe potential adverse events but rarely quantify their likelihood, limiting their usefulness for probabilistic analysis. A central obstacle is…
Future-as-Label: Scalable Supervision from Real-World Outcomes
Benjamin Turtel, Paul Wilczewski, Danny Franklin +1
Time creates free supervision: forecasts about real-world events resolve to verifiable outcomes. The passage of time provides labels that require no annotation. To exploit this str…
Outcome-based Reinforcement Learning to Predict the Future
Benjamin Turtel, Danny Franklin, Kris Skotheim +2
Reinforcement Learning with Verifiable Rewards (RLVR) has been an effective approach for improving Large Language Models' reasoning in domains such as coding and mathematics. Here,…
LLMs Can Teach Themselves to Better Predict the Future
Benjamin Turtel, Danny Franklin, Philipp Schoenegger
We present an outcome-driven fine-tuning framework that enhances the forecasting capabilities of large language models (LLMs) without relying on human-curated reasoning samples. Ou…