How does the performance of DeepSeek-R1 compare to o1-preview on the APPS benchmark when evaluated under negat

Abstract

Abstract: Recently, there is a high demand for deploying DeepSeek-R1 and V3 locally, possibly because the official service often suffers from being busy and some organizations have data privacy concerns. While single-machine deployment offers infrastructure simplicity, the models' 671B FP8 parameter configuration exceeds the practical memory limits of a standard 8-GPU machine. Quantization is a widely used technique that helps reduce model memory consumption. However, it is unclear what the performance of DeepSeek-R1 and V3 will be after being quantized. This technical report presents the first quantita

Research Question

How does the performance of DeepSeek-R1 compare to o1-preview on the APPS benchmark when evaluated under negation-based token perturbations, and does the S* selection mechanism mitigate observed robustness gaps?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance