Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 28·8 min read
EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA
TL;DR
Open-ended question-answering systems struggle to learn effectively from training data because they treat all correct and incorrect answers equally, missing nuance in learning signals. EAPO (Entropy-Driven Adaptive Positive-Negative Sample Weighting) dynamically adjusts how much weight the model gives to different training examples based on prediction uncertainty.
✦ Why It Matters
Engineers can adopt entropy-driven sample weighting to train QA systems more efficiently, reducing compute costs while improving accuracy on challenging examples.
Key Takeaways
- Use entropy-based weighting to prioritize uncertain predictions during training instead of treating all examples equally.
- Apply adaptive sample reweighting in reinforcement learning pipelines to reduce wasted computation on easy examples.
- Focus model learning on genuinely difficult questions by dynamically adjusting positive and negative sample importance.
- Measure prediction uncertainty to identify which training examples provide the most learning signal for QA tasks.
Full Summary
Open-ended QA systems—models that answer questions with free-form text rather than multiple choice—face a learning efficiency problem: standard training treats all positive examples (correct answers) and negative examples (incorrect answers) identically, ignoring which examples are most informative for improvement. EAPO addresses this by using entropy (a measure of prediction uncertainty) to adaptively reweight training samples during policy optimization, a reinforcement-learning technique that adjusts model behavior based on reward signals.
The approach assigns higher weights to examples where the model is uncertain, forcing it to focus learning effort on genuinely difficult cases rather than easy ones it already handles well. By dynamically balancing positive and negative sample importance based on model confidence, EAPO reduces wasted computation on redundant training signals.
Empirical results demonstrate measurable gains in QA accuracy and sample efficiency compared to uniform weighting baselines, with particular improvements on harder questions where adaptive focus matters most.