OpenForecaster-8B

OpenForecaster-8B is a specialized language model for forecasting and predicting future events. This model is post-trained from Qwen3-8B using reinforcement learning on the OpenForesight dataset.

Model Description

OpenForecaster-8B is designed to make calibrated predictions on open-ended questions about future events. The model has been trained to:

Provide calibrated confidence estimates when asked to do so
Reason about uncertainty and future scenarios
Leverage retrieved information (when provided in context) to improve predictions

Training

This model was trained on the OpenForesight dataset, which contains over 52,000 forecasting questions generated from global news events. The training was done using GRPO optimizing a joint reward function combining accuracy and brier score.

Base Model: Qwen3-8B
Training Dataset: OpenForesight

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "nikhilchandak/OpenForecaster-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# template 
prompt = "What is the likelihood that [future event] will occur by [date]?"
# example
prompt = "Who will become the next Prime Minister of India based on the general election to be held in 2029? Provide specific predictions with probabilities."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=8192)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(prediction)

Performance

OpenForecaster-8B achieves competitive performance with much larger models like DeepSeek-v3 and Qwen3-235B-A22B on forecasting benchmarks. Key improvements include:

Improved Accuracy: Better prediction of future events
Better Calibration: More reliable confidence estimates
Enhanced Consistency: Reduced logical violations in predictions

More Information

For more details about the model, training process, and evaluation results, please visit our website:

🌐 https://openforecaster.github.io

Citation

@article{openforesight2025,
  title  = {Scaling Open-Ended Reasoning To Predict the Future},
  author = {Chandak, Nikhil and Goel, Shashwat and Prabhu, Ameya and Hardt, Moritz and Geiping, Jonas},
  year   = {2025}
}