Scaling LLM Inference with Optimized Sample Compute Allocation (2410.22480v1)

Abstract: Sampling is a basic operation in many inference-time algorithms of LLMs. To scale up inference efficiently with a limited compute, it is crucial to find an optimal allocation for sample compute budgets: Which sampling configurations (model, temperature, language, etc.) do we use? How many samples do we generate in each configuration? We formulate these choices as a learning problem and propose OSCA, an algorithm that Optimizes Sample Compute Allocation by finding an optimal mix of different inference configurations. Our experiments show that with our learned mixed allocation, we can achieve accuracy better than the best single configuration with 128x less compute on code generation and 25x less compute on 4 reasoning tasks. OSCA is also shown to be effective in agentic workflows beyond single-turn tasks, achieving a better accuracy on SWE-Bench with 3x less compute than the default configuration. Our code and generations are released at https://github.com/LeiLiLab/OSCA.

• The paper demonstrates that Osca’s hill-climbing approach significantly enhances LLM inference accuracy while reducing computational costs.
• The methodology employs a mixed compute allocation strategy across multiple configurations to address challenges in code generation and reasoning tasks.
• Empirical evaluations on benchmarks like LiveCodeBench reveal notable improvements in pass rates and scalability compared to standard allocation methods.

An Analytical Examination of Osca: Optimizing Sample Compute Allocation for LLM Inference

The paper "Scaling LLM Inference with Optimized Sample Compute Allocation" presents the Osca algorithm, which strategically optimizes sample compute allocation in LLM inference tasks. The primary focus is on improving accuracy while reducing computational resources, explicitly targeting issues in code generation and reasoning tasks. Through an in-depth exploration of the design and performance of Osca, this document aims to detail the critical insights and potential ramifications of this research for advanced applications in artificial intelligence.

Key Contributions and Methodological Insights

The prevalent challenge in LLM inference is the effective allocation of limited compute resources across various sampling configurations encompassing model choice, temperature settings, output language, and prompt specifications. Osca addresses this by formulating the problem as an optimization task, proposing an algorithm that leverages a hill-climbing approach to find an optimal distribution maximizing accuracy. This is particularly vital in tasks where a monolithic configuration might fall short in addressing diverse problem types effectively.

Osca's effectiveness is underscored by its demonstrated ability to outperform traditional pure and uniformly mixed compute allocations. The paper provides compelling evidence of its prowess through quantitative assessments on benchmarks like LiveCodeBench and LiveBench. For instance, it achieves a significant improvement in pass rates using a fraction of the compute resources required by standard methods.

Evaluative Metrics and Empirical Results

The experimental framework utilized in this paper revolves around comparing Osca's optimized mixed allocation against baseline configurations, such as default pure, optimal pure, and uniform mixed allocations. Results notably highlight Osca's ability to maintain superior accuracy growth with increasingly larger compute budgets. This scaling advantage is crucial for applications constrained by computational efficiency and cost-effectiveness.

Critically, Osca's success is not contingent on a precise tuning of hyperparameters across diverse tasks; it broadens the exploration space by considering multiple models and temperatures, which are typically fixed in routine inference configurations. The robustness of these allocations in variable settings suggests potential adaptability across different LLM applications, promoting holistic model utilization strategies.

Theoretical and Practical Implications

Osca contributes substantively to the theoretical understanding of inference optimization in LLMs by demonstrating the benefits of flexible sampling strategies. From a practical viewpoint, its applicability extends beyond straightforward single-turn tasks. In agentic workflows for complex benchmarks such as SWE-Bench, Osca provides tangible performance enhancements, illustrating its capability to integrate seamlessly into multi-modal and multi-stage LLM frameworks.

The algorithm introduces a prospective paradigm in inference compute management where adaptive strategies replace static configurations, particularly beneficial in real-time and resource-constrained environments. Future exploration might focus on extending these principles to additional hyperparameters and investigating its impact when scaling computational limits further.

Concluding Remarks

The advancements posited by the Osca algorithm offer a nuanced perspective on LLM optimization, presenting a compelling case for the strategic allocation of compute resources. The paper convincingly argues for the necessity and efficacy of mixed allocation models, reinforcing the idea that a one-size-fits-all solution is seldom optimal in complex LLM deployments. Thus, Osca's methodology aligns with ongoing trends toward more adaptive, context-sensitive AI systems capable of nuanced decision-making and resource allocation. Researchers and practitioners within the AI community will find the insights and methodologies introduced here to be of considerable value for upcoming developments in optimizing large-scale LLMs.