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OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises with a better understanding of how these models make decisions.\u00a0<\/p>\n
Understanding how models choose to respond, a big selling point of reasoning models for enterprises, can provide a level of trust for organizations when they turn to AI models for insights.\u00a0<\/p>\n
The method called for OpenAI scientists and researchers to look at and evaluate models not by analyzing post-training performance, but by adding interpretability or understanding through sparse circuits.<\/p>\n
OpenAI notes that much of the opacity of AI models stems from how most models are designed, so to gain a better understanding of model behavior, they must create workarounds.\u00a0<\/p>\n
\u201cNeural networks power today\u2019s most capable AI systems, but they remain difficult to understand,\u201d OpenAI wrote in a blog post. \u201cWe don\u2019t write these models with explicit step-by-step instructions. Instead, they learn by adjusting billions of internal connections or weights until they master a task. We design the rules of training, but not the specific behaviors that emerge, and the result is a dense web of connections that no human can easily decipher.\u201d<\/p>\n
To enhance the interpretability of the mix, OpenAI examined an architecture that trains untangled neural networks, making them simpler to understand. The team trained language models with a similar architecture to existing models, such as GPT-2, using the same training schema.\u00a0<\/p>\n
The result: improved interpretability.\u00a0<\/p>\n
Understanding how models work, giving us insight into how they're making their determinations, is important because these have a real-world impact, OpenAI says.\u00a0\u00a0<\/p>\n
The company defines interpretability as \u201cmethods that help us understand why a model produced a given output.\u201d There are several ways to achieve interpretability: chain-of-thought interpretability, which reasoning models often leverage, and mechanistic interpretability, which involves reverse-engineering a model\u2019s mathematical structure.<\/p>\n
OpenAI focused on improving mechanistic interpretability, which it said \u201chas so far been less immediately useful, but in principle, could offer a more complete explanation of the model\u2019s behavior.\u201d<\/p>\n
\u201cBy seeking to explain model behavior at the most granular level, mechanistic interpretability can make fewer assumptions and give us more confidence. But the path from low-level details to explanations of complex behaviors is much longer and more difficult,\u201d according to OpenAI.\u00a0<\/p>\n
Better interpretability allows for better oversight and gives early warning signs if the model\u2019s behavior no longer aligns with policy.\u00a0<\/p>\n
OpenAI noted that improving mechanistic interpretability \u201cis a very ambitious bet,\u201d but research on sparse networks has improved this.\u00a0<\/p>\n
To untangle the mess of connections a model makes, OpenAI first cut most of these connections. Since transformer models like GPT-2 have thousands of connections, the team had to \u201czero out\u201d these circuits. Each will only talk to a select number, so the connections become more orderly.<\/p>\n
Next, the team ran \u201ccircuit tracing\u201d on tasks to create groupings of interpretable circuits. The last task involved pruning the model \u201cto obtain the smallest circuit which achieves a target loss on the target distribution,\u201d according to OpenAI. It targeted a loss of 0.15 to isolate the exact nodes and weights responsible for behaviors.\u00a0<\/p>\n
\u201cWe show that pruning our weight-sparse models yields roughly 16-fold smaller circuits on our tasks than pruning dense models of comparable pretraining loss. We are also able to construct arbitrarily accurate circuits at the cost of more edges. This shows that circuits for simple behaviors are substantially more disentangled and localizable in weight-sparse models than dense models,\u201d the report said.\u00a0<\/p>\n
Although OpenAI managed to create sparse models that are easier to understand, these remain significantly smaller than most foundation models used by enterprises. Enterprises increasingly use small models, but frontier models, such as its flagship GPT-5.1, will still benefit from improved interpretability down the line.\u00a0<\/p>\n
Other model developers also aim to understand how their AI models think. Anthropic, which has been researching interpretability for some time, recently revealed that it had \u201chacked\u201d Claude\u2019s brain \u2014 and Claude noticed. Meta also is working to find out how reasoning models make their decisions.\u00a0<\/p>\n
As more enterprises turn to AI models to help make consequential decisions for their business, and eventually customers, research into understanding how models think would give the clarity many organizations need to trust models more.\u00a0<\/p>\n<\/p>\n