AI labs want to use AI to speed up their own AI development. OpenAI says the new GPT-5.6 Sol model does this better than anything before it.
OpenAI's new flagship model, GPT-5.6 Sol, independently post-trained the smaller model Luna, according to the company. After Luna's initial pre-training, Sol optimized it for specific skills and behaviors on its own.
A researcher gave Sol a "fairly under-specified prompt" through the Codex platform. The instructions told the model to find the right training configurations, pick suitable GPUs, launch the training script, and verify everything was running correctly.
"Previously this is something that a team of senior researchers may have worked on at OpenAI, and now it really feels like the automated researcher is pretty close," OpenAI researcher Kathy Shi said during the presentation.
To measure these abilities directly, OpenAI built an internal evaluation suite based on real-world AI research tasks. Those tasks include debugging research systems, optimizing kernels and training recipes, running machine learning experiments, and improving another model.
GPT-5.6 Sol scores 16.2 points higher than GPT-5.5 on the aggregated RSI (Recursive Self-Improvement) index, according to OpenAI. Sol sits at the top of the benchmark's model hierarchy, followed by the Terra and Luna variants, then GPT-5.5 and GPT-5.4.
Recursive Self-Improvement in AI research refers to an AI system's ability to make itself better, where each round of gains makes the system even more capable of improving itself. That creates a feedback loop. The term has long been central to AI safety research because a system that can recursively improve itself could, in theory, trigger a rapid explosion in capability.
OpenAI rival Anthropic stressed in early June that full recursive self-improvement hasn't been achieved yet but "could come sooner than most institutions are prepared for." Full RSI means an AI system that designs its own successor without human help. According to Anthropic, Claude can now handle incremental work between major paradigm shifts, and humans are responsible for only a single-digit percentage of directional decisions.
OpenAI says its researchers use GPT-5.6 Sol across the entire development cycle, from debugging and optimizing training systems to running experiments and reading results. Even during internal testing, average daily token output per active researcher more than doubled the previous peak set by GPT-5.5. Pull requests and experiments per researcher went up too, letting teams turn ideas into results faster.
The company's own adoption numbers from the past six months paint a predictably rosy picture. The share of compute allocated to internal coding inference grew 100x, while agent-based token usage jumped roughly 22x. OpenAI acknowledges these metrics don't directly measure research progress but says they show how fast AI-assisted work is scaling.