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bturtel

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A small economic forecaster trained from raw Fed PDFs beat GPT-5

blog.lightningrod.ai
5 points·by bturtel·3 miesiące temu·0 comments

Show HN: Open-source LLM and dataset for sports forecasting (Pro Golf)

huggingface.co
7 points·by bturtel·5 miesięcy temu·0 comments

Show HN: Trained an LLM to predict "What will Trump do?"

huggingface.co
10 points·by bturtel·5 miesięcy temu·2 comments

What can't be automated? The Last Human Bottleneck

bturtel.substack.com
2 points·by bturtel·5 miesięcy temu·0 comments

Future-as-Label: Scalable Supervision from Real-World Outcomes

arxiv.org
17 points·by bturtel·6 miesięcy temu·0 comments

TMLR: Outcome-Based Reinforcement Learning to Predict the Future

openreview.net
4 points·by bturtel·7 miesięcy temu·1 comments

Natural Selection Is Already Shaping AI

bturtel.substack.com
2 points·by bturtel·8 miesięcy temu·0 comments

Flooding the AI Frontier

bturtel.substack.com
2 points·by bturtel·10 miesięcy temu·0 comments

comments

bturtel
·5 miesięcy temu·discuss
Great question! It's probabilistic so not really "right vs wrong" on any single question, but who better estimated the likelihood. One big difference shows up when there's no useful context - we ran the same eval WITHOUT including any useful up-to-date context with questions. In this case, GPT-5 stays overconfident and its BSS drops to -11.3% (vs -4.3% ours) - worse than just guessing the base rate. So one advantage of the RL training is just learning to know what you don't know, and identify when there's real signal.