How to Solve the Data Scarcity Problem in Large Models? A Practical Analysis of OpenClaw + IPPeak Residential Proxies

As large models continue to advance, data has evolved from a “supporting resource” into a “core bottleneck.” With tools like OpenClaw, more teams are building automated data collection systems for model training, evaluation, and optimization. However, the reality is that high-quality data is not easy to obtain. While public data is abundant, large-scale acquisition often encounters issues such as access restrictions, data bias, and instability. This is the so-called “data scarcity” problem—not a lack of data, but the difficulty in consistently and reliably obtaining high-quality data.

Where Are the Real Challenges in Data Acquisition?

In practice, the main challenges in data collection often lie at the access layer. If request sources are too concentrated or access patterns appear abnormal, they can easily be detected by target websites, triggering restriction mechanisms. This can directly lead to interruptions or incomplete data collection. In addition, regional differences in data distribution also play a role. Without the ability to collect data from multiple regions, training datasets may lack diversity.

Why Residential Proxies Have Become Key Infrastructure

To address these challenges, more teams are turning to residential proxies as a core component of their data collection infrastructure. Residential IPs originate from real user networks, making access behavior appear more natural and reducing the likelihood of detection. This allows data collection to proceed in a more stable environment while improving success rates.

IPPeak offers a mature solution in this space, integrating over 80 million real residential IPs across more than 195 countries and regions, supporting multi-region data acquisition needs. In practical applications, it achieves a connection success rate of up to 99.95% with an average response time of around 0.5 seconds, providing strong stability for large-scale data collection.

Synergy with Automated Data Collection Tools

Tools like OpenClaw excel at automating tasks. However, without a stable underlying network, even the best tools cannot operate reliably over time. Only when automated data collection tools are combined with high-quality proxy networks can a complete data acquisition system be formed: the tools handle execution, while proxies provide stable access paths. This combination is becoming the mainstream architecture for large-model data acquisition.

Conclusion

In the era of large models, “data scarcity” is essentially a problem of acquisition capability. By combining automation tools with high-quality proxy networks, teams can build a more stable data acquisition system and provide continuous support for model training.