Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 27·8 min read
Completion vs Optimality: Policy Gradient in Long-Horizon Cumulative-Damage Problems
TL;DR
Long-horizon decision problems suffer when locally good actions cause cumulative damage over time, breaking standard policy-gradient methods. Researchers decomposed this failure into two independent problems: completion (reaching the goal without early exit) and optimality (finding the best policy once completion is achieved).
✦ Why It Matters
Engineers can diagnose policy-gradient failures in long-horizon tasks by identifying whether problems stem from incomplete trajectories or suboptimal solutions.
Key Takeaways
- Differentiate between completion and optimality in decision-making.
- Use Proximal Policy Optimization for better policy-gradient performance.
- Be aware of the optimality gap when allowing horizon access.
- Test predictions in varied environments to validate findings.
How It Works
The study introduces a decomposition of policy-gradient methods into completion and optimality components. By analyzing the effects of horizon access and penalty mechanisms, the authors demonstrate how these factors influence decision-making outcomes.
They utilize PPO with a linear soft penalty to explore these dynamics, revealing that while horizon access can enhance completion, it may simultaneously hinder optimality.