Accelerating the search for antimicrobial peptides with Google Antigravity
deepmind.google·2h ago
AI Research
arXiv CS.AI·3d ago·5 min read
Hindsight Structured Advice Distillation: Steering Executor Agents with Advisors Trained on Their Failures and Task Characteristics
TL;DR
Hindsight Structured Advice Distillation (HSAD) enhances executor agents by using a small advisor model that generates tailored guidance based on past failures. This approach improved the qualification rate of a financial research agent from 52.9% to 56.6% at a lower cost per query.
✦ Why It Matters
Implement HSAD to enhance your AI agents' performance without incurring high retraining costs.
Key Takeaways
- Use HSAD to improve agent performance on specific tasks.
- Train a smaller advisor model to generate tailored guidance.
- Leverage past failures to inform future advice generation.
- Reduce operational costs while enhancing output quality.
How It Works
HSAD involves training a small advisor model that analyzes the executor's past failures and task characteristics. It generates structured advice based on this analysis, which is then appended to the executor's prompt.
This method allows the advisor to provide context-specific guidance without needing to retrain the larger executor model, thus saving time and resources.