| name | one-sample-polymath-learning |
| title | One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.03111 |
| keywords | ["Reinforcement Learning","Data Efficiency","Cross-Domain Learning"] |
| description | Demonstrate that a single strategically engineered training sample can improve reasoning across multiple domains. Polymath learning shows sample quality and multidisciplinary design matter more than quantity, enabling extreme data efficiency in RL training. |
Overview
This skill extracts and operationalizes key insights from the research paper. See the arxiv link for full technical details, proofs, and comprehensive benchmarks.
When to Use
- Research and development in reinforcement learning
- Implementing domain-specific techniques
- Improving system performance
When NOT to Use
- When simpler approaches suffice
- In resource-constrained environments without GPU capacity
- Domains where the technique was not validated
Key Contribution
This paper presents a novel approach to the field by introducing novel techniques. The key innovation enables practical benefits in real-world scenarios.
Implementation Strategy
- Review the full paper for mathematical formulations
- Consult the experimental section for configuration details
- Adapt the approach to your specific domain
- Validate on relevant benchmarks
- Tune hyperparameters for your use case
Performance Indicators
- Consistent improvements demonstrated across multiple benchmarks
- Works across diverse model sizes and architectures
- Practical deployment feasible with standard hardware
References
Detailed methodology, ablations, and full results available in the original paper at https://arxiv.org/abs/2601.03111.
