Adaptive Compute Allocation for LLMs via Bandit Learning
A novel strategy presented in arXiv (2506.12721v2) addresses compute allocation during testing for large language models by framing it as a bandit learning challenge. Rather than distributing compute evenly across all queries, adaptive algorithms dynamically assess the difficulty of each query, assigning additional resources to more complex ones while ensuring that simpler queries remain accurate. This technique also emphasizes focusing on solvable cases within difficult queries, minimizing unnecessary compute on those that cannot be solved. Theoretical evidence demonstrates enhanced compute efficiency compared to uniform allocation, and empirical tests confirm its effectiveness on math and code benchmarks.
Key facts
- arXiv paper 2506.12721v2 proposes adaptive test-time compute allocation for LLMs.
- Formulates compute allocation as a bandit learning problem.
- Algorithms estimate query difficulty dynamically.
- More compute allocated to challenging queries, less to easier ones.
- Among hard queries, prioritizes solvable instances over unsolvable.
- Theoretically proven to be more efficient than uniform allocation.
- Empirically validated on math and code benchmarks.
Entities
Institutions
- arXiv