A team of researchers has introduced a new approach to improving artificial intelligence (AI) reasoning that doesn’t rely on expanding model size.
Their method, called “Sample, Scrutinize and Scale”, enhances AI performance at inference time by generating multiple candidate responses and selecting the most reliable one through self-verification. Early results indicate that this method could give models like Gemini v1.5 Pro an edge over OpenAI’s o1-Preview in benchmark reasoning tests.
However, the method is already sparking debate. Some experts argue that the computational overhead of running multiple inferences per query could limit its real-world viability. Others question whether AI can effectively “verify itself” in a meaningful way.
Beyond Bigger Models: A Shift in AI Scaling
For years, AI advancements have relied on increasing the number of parameters, training data, and compute power. This approach, based on neural scaling laws, has fueled the rapid progress of large language models. However, recent studies and the poor relative performance of OpenAI’s latest GPT-4.5 model suggest that scaling is now delivering diminishing returns despite soaring costs, pushing researchers to seek alternative methods.
The Sample, Scrutinize and Scale method proposes a different approach by optimizing AI performance during inference rather than training.
Instead of producing a single response, AI models generate multiple outputs, cross-check them, and select the best answer. This process creates what researchers call an “implicit scaling effect”, making models appear more capable without additional training data or larger architectures.
Additionally, the method incorporates response rewriting, in which the AI reformulates its answers in different formats to improve verification accuracy. According to the study, this technique significantly improves results in multi-step reasoning benchmarks such as MMLU and BigBench-Hard, outperforming single-response models.
Verification Challenges and Skepticism
AI’s biggest limitation today is its struggle with self-verification. Large models, including GPT-4o, GPT-4.5 or Claude 3.7 Sonnet, often generate convincing but inaccurate responses, a problem known as hallucination.
The researchers behind Sample, Scrutinize and Scale argue that structured verification could mitigate these errors.
To test this, the researchers introduced a new benchmark to evaluate how well models verify their own responses. Their results suggest that this method improves accuracy in reasoning tasks compared to conventional inference models.
However, questions remain about the computational efficiency of this approach. Running multiple inferences for every query increases processing demands, which could make this method impractical for real-time applications like search engines and voice assistants.
How AI Companies Are Adapting to Scaling Challenges
With the limitations of traditional scaling becoming more apparent, major AI labs and companies are exploring alternative approaches:
- DeepMind is testing genetic algorithms for inference-time search, refining model reasoning iteratively.
- IBM Research is combining probabilistic reasoning models with LLMs to improve inference accuracy.
- OpenAI and others are working on test-time compute, which dynamically adjusts processing power rather than scaling models during training.
Meanwhile, hardware manufacturers are responding to the increased demand for efficient inference solutions. NVIDIA’s latest AI chips are optimized for inference workloads, potentially aligning with verification-based scaling approaches.
Smarter Scaling or Just Another Compute Burden?
While Sample, Scrutinize and Scale offers a new perspective on AI scaling, its feasibility remains uncertain. The increased processing power required for multiple inferences per query raises concerns about latency, scalability, and energy consumption.
For applications where accuracy is more important than speed—such as scientific research or legal document review—this approach may provide meaningful benefits. But for more latency-sensitive environments, the added compute cost might outweigh its advantages.
The focus is shifting from simply scaling models up to finding more efficient ways to optimize reasoning. Whether verification-based scaling becomes an industry standard or remains a niche experiment will depend on how companies balance accuracy, processing speed, and energy demands in the coming years.
