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Large language models (LLMs) have demonstrated remarkable performance across multiple domains, driven by scaling laws highlighting the relationship between model size, training computation, and performance. Despite significant advancements in model scaling, a critical gap exists in comprehending how computational resources during inference impact model performance post-training. The complexity arises from balancing performance improvements against the increasing computational costs, associated with advanced inference techniques. Moreover, understanding the trade-offs between performance gains and computational expenses is crucial for developing more efficient and effective LLM inference strategies.
Existing research on LLMs has explored various strategies to enhance mathematical reasoning and problem-solving capabilities. They focus on generating step-by-step solutions, which are expanded to include solution verification and ranking methodologies. Inference strategies have ranged from deterministic methods like greedy decoding and beam search to more dynamic sampling algorithms that introduce diversity in generated sequences. More advanced techniques have emerged, including majority voting, weighted majority voting, and search-based algorithms like Monte Carlo Tree Search (MCTS). Process Reward Models (PRMs) have also gained prominence, providing a mechanism to assign rewards to intermediate reasoning steps and guide the multi-step problem-solving process.
Researchers from the Institute for Interdisciplinary Information Sciences at Tsinghua University and the School of Computer Science at Carnegie Mellon University have presented a comprehensive study on inference scaling laws and compute-optimal inference strategies. The research aims to explore the critical trade-offs between model sizes and token generation across various inference methodologies. By investigating cost-performance relationships, the researchers examine inference approaches like greedy search, majority voting, best-of-n, weighted voting, and two distinct tree search algorithms. The study reveals that smaller models can outperform larger models when equipped with advanced inference algorithms, challenging conventional assumptions about model scaling and computational efficiency.
The research methodology is structured around two primary experimental questions investigating compute-optimal inference strategies for mathematical problem-solving. Two mathematical datasets MATH, and GSM8K are selected. The experimental design uses multiple policy models, including Pythia models, math-specialized Llemma models, and Mistral-7B, to explore performance variations across different model sizes and architectures. A consistent Llemma-34B reward model fine-tuned on the Math-Shepherd synthetic dataset, is utilized to evaluate solution quality. Each experimental configuration is executed multiple times to ensure robust and reliable results, allowing comprehensive statistical analysis of performance scaling and computational efficiency across different inference strategies and model sizes.
The results show that Llemma-7B achieves competitive accuracy with Llemma-34B while requiring approximately 50% less computational resources. This finding suggests that smaller models when paired with appropriate inference strategies, can deliver more favorable cost-performance trade-offs than the larger models. Moreover, the REBASE inference strategy consistently proves Pareto-optimal across various settings and outperforms sampling-based methods and traditional tree search algorithms like MCTS. Notably, REBASE achieves higher accuracy with substantially lower computational budgets, a novel finding that challenges previous assumptions about computational complexity in inference strategies.
In conclusion, researchers provide critical insights into compute-optimal inference strategies for LLMs, offering three fundamental conclusions. First, the study demonstrates that smaller models using complex inference techniques can outperform larger models within constrained computational budgets. Second, the research reveals the fundamental limitations of sampling-based majority voting strategies. Third, the novel REBASE tree search method emerges as a groundbreaking inference strategy, proving Pareto-optimal across tested compute budgets and surpassing established methods. Lastly, the limitations of this research include its focus on mathematical problem-solving and proposing future research directions exploring inference scaling laws across diverse task domains.
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“}]] [[{“value”:”Large language models (LLMs) have demonstrated remarkable performance across multiple domains, driven by scaling laws highlighting the relationship between model size, training computation, and performance. Despite significant advancements in model scaling, a critical gap exists in comprehending how computational resources during inference impact model performance post-training. The complexity arises from balancing performance improvements against the
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