This Ai Paper Introduces A Machine Learning Framework To Estimate The Inference Budget For Self-consistency And Genrms (generative Reward Models)

Large Language Models (LLMs) person demonstrated important advancements successful reasoning capabilities crossed divers domains, including mathematics and science. However, improving these reasoning abilities astatine trial clip remains a situation researchers are actively addressing. The superior attraction lies successful processing methods to standard test-time compute efficaciously while maximising reasoning performance. Current methodologies see generating aggregate chains-of-thought (CoTs) solutions for problems and implementing voting aliases action mechanisms to place nan champion solutions. Although these approaches person shown promise, they often require sizeable computational resources and whitethorn not consistently place optimal solutions erstwhile incorrect reasoning pathways dominate. Finding businesslike ways to heighten LLM reasoning while minimizing computational overhead represents a captious situation for nan field’s advancement.

Previous investigation has explored various approaches to heighten LLM reasoning capabilities. Generative Reward Models (GenRM) person emerged arsenic a promising technique, framing verification arsenic a next-token prediction task. These models alteration test-time scaling by generating aggregate verification chains-of-thought and aggregating their verdicts to people solutions. Initial comparisons betwixt GenRM pinch Best-of-N (BoN) action and Self-Consistency (SC) showed that GenRM appeared much efficient, achieving comparable capacity pinch less solution candidates. However, these evaluations were conducted pinch fixed numbers of solutions alternatively than fixed computational budgets. This methodology creates misleading conclusions successful applicable scenarios wherever conclusion compute is limited, arsenic it fails to relationship for nan important computational costs associated pinch generating aggregate verifications for each campaigner solution. The cardinal limitation of existing approaches is their nonaccomplishment to see nan existent computational ratio erstwhile comparing verification-based methods pinch simpler mostly voting techniques.

The projected method introduces a broad model for accurately estimating nan conclusion computational fund required by Self-Consistency and GenRMs. This model enables a fair, compute-matched analysis that compares these test-time scaling strategies nether fixed computational constraints. The attack assumes a azygous Large Language Model serves dual functions arsenic some nan solution generator and generative verifier, pinch verification capabilities activated either done specialized prompting aliases task-specific fine-tuning. By establishing this unified framework, researchers tin systematically analyse nan capacity trade-offs betwixt generating much solution candidates for Self-Consistency versus allocating compute resources to verification processes successful GenRMs. The comparative study focuses connected measuring effectiveness based connected nan full number of solutions and verifications generated by nan LLM, providing clear metrics for computational ratio crossed different reasoning approaches.

The methodology employs a compute-matched study model pinch a elaborate architectural creation for comparing test-time scaling strategies. For an autoregressive LLM pinch P parameters performing 2P FLOPs per output token, nan full conclusion compute is calculated utilizing nan look C(S, V) = S(1+λV), wherever S represents nan number of solutions, V nan number of verifications, and λ nan ratio of tokens per verification to tokens per solution. This model enables systematic information of some Self-Consistency and Generative Reward Models nether balanced computational constraints. The architecture includes scaling solutions for SC crossed S ∈ {2^0, 2^1, …, 2^N} and evaluating GenRM crossed combinations of solutions and verifications S, V ∈ {S × V}. Also, nan investigation introduces conclusion scaling laws for GenRM done a six-step methodology that determines optimal allocation betwixt solutions and verifications. This process involves computing occurrence rates crossed expanding verification counts, plotting results against compute budgets, and fitting powerfulness laws to found relationships betwixt optimal solution counts (S_opt ∝ C^a) and verification counts (V_opt ∝ C^b).

The results show a clear shape erstwhile comparing nan capacity of Generative Reward Models against Self-Consistency crossed different computational budgets. SC exhibits superior capacity successful low-compute scenarios, making it nan much businesslike prime erstwhile computational resources are limited. Conversely, GenRM originates to outperform SC only aft reaching astir 8× nan computational budget, requiring an further 128× conclusion compute to execute a humble capacity betterment of 3.8% complete SC. These findings beryllium robust crossed divers experimental conditions, including various exemplary families specified arsenic Llama and Qwen, different exemplary sizes ranging from 7B to 70B parameters, specialized reasoning models for illustration QwQ-32B, and different reasoning tasks, including mathematics. The capacity patterns stay accordant sloppy of nan circumstantial LLM architecture employed, indicating nan wide applicability of these comparative insights crossed nan spectrum of connection models and reasoning tasks.

The study introduces GenRMs arsenic an innovative attack to scaling test-time compute done verification processes. Previous investigation demonstrated that scaling some solutions and verifications could outperform SC, but often neglected to relationship for nan computational costs of verification. This broad investigation reveals a clear pattern: SC proves much effective astatine little computational budgets, while GenRMs present superior capacity erstwhile higher computational resources are available. These findings support consistency crossed aggregate exemplary families, including specialized reasoning models, various parameter sizes from 7B to 70B, and divers reasoning tasks. In addition, nan investigation establishes robust conclusion scaling laws that optimize fund allocation betwixt solution procreation and verification processes wrong GenRM frameworks. These insights supply valuable applicable guidance for researchers and practitioners seeking to instrumentality compute-efficient scaling strategies to maximize reasoning capacity successful ample connection models.

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