Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.Subscribe nowHuawei’s HiFloat4 training format beats Western-developed MXFP4 in Ascend chip bakeoff:…Could this also be a symptom of the impact of export controls in driving Chinese interest towards maximizing training and inference efficiency?

Perhaps…Huawei researchers have tested out HiFloat4, a 4-bit precision format for AI training and inference, against MXFP4, an Open Compute Project 4-bit format, and found that HiFloat4 is superior. This is interesting because it correlates to a broader level of interest in Chinese companies seeking to develop their own low-precision data formats explicitly coupled with their own hardware platforms. “Our goal is to enable efficient FP4 LLM pretraining on specialized AI accelerators with strict power constraints.

We focus on Huawei Ascend NPUs, which are domain-specific accelerators designed for deep learning workloads,” they write.What they tested: In this paper, the authors train 3 model types on HuaWei Ascend chips - OpenPangu-1B, Llama3-8B, and Qwen3-MoE-30B. In tests, the bigger they make the models, the better HiFloat4 does at reducing its loss error on these models relative to a BF16 baseline - and in all cases it does better than MXFP4. What they found: “We conduct a systematic evaluation of the HiFloat4 (HiF4) format and show that it achieves lower relative loss (≈ 1.0%) compared to MXFP4 (≈ 1.5%) when measured against a full-precision baseline,” they write.

“HiF4 consistently achieves significantly lower relative error compared to MXFP4. For Llama and Qwen, HiF4 attains an error gap of less than 1% with respect to the baseline… HiF4 gets within ~1% of BF16 loss with only RHT as a stabilization trick, while MXFP4 needs RHT + stochastic rounding + truncation-free scaling to get to ~1.5%.”Why this matters - symptom of hardware maturity, and a possible influence of export controls: HiFloat4 is an even lower precision version of HiFloat8 (#386), and generally maps to the fact that Huawei (and Chinese chipmakers in general) is continually trying to eke as much efficiency out of its chips as possible.

This comes against the broader background of export controls where China is being starved of frontier compute due to not being able to access H100s etc in large volume, thus making it even more valuable to improve the efficiency of its homegrown chips by carefully developing low-precision formats to map to its own hardware. Read more: HiFloat4 Format for Language Model Pre-training on Ascend NPUs (arXiv).***Anthropic shows how to automate AI safety R&D:…Very early and tentative signs that it’s possible to automate AI research…For many people working in AI, the ultimate goal is to automate the art of AI research itself.

Now, researchers with the Anthropic Fellows Program and Anthropic have published some early warning signs that automating AI research is possible today - though many caveats apply. “We ask: can Claude develop, test, and analyze alignment ideas of its own?” the researchers write. They succeed and are able to successfully build “autonomous AI agents that propose ideas, run experiments, and iterate on an open research problem: how to train a strong model using only a weaker model’s supervision.

These agents outperform human researchers, suggesting that automating this kind of research is already practical.”Weak-to-strong supervision: The domain the researchers test on is weak-to-strong supervision, which is roughly the idea of seeing if a dumber thing can effectively supervise a larger thing in doing a hard task.Overall results - automated research beats humans: They used people to create a weak-to-strong baseline by seeing how well they could get a good ‘performance gap recovered’ (PGR) score on a generalization task. The higher the number, the better. “Two of our researchers spent seven days iterating on four of the most promising generalization methods from prior research.

On the open-weights models we tested (Qwen 3-4B-Base as the strong model, Qwen 1.5-0.5B-Chat as the weak teacher), the humans recovered 23% of the total performance gap (i.e., achieved a PGR of 0.23),” they write. “Claude improved on this result dramatically. After five further days (and 800 cumulative hours of research), the AARs closed almost the entire remaining performance gap, achieving a final PGR of 0.97.

This cost about $18,000 in tokens and model training expenses, or $22 per AAR-hour.” Additionally, “the AARs’ most effective method successfully generalized to both new datasets, with PGRs of 0.94 on math and 0.47 on coding (which was still double the human baseline).”How they did it: “We launch a team of parallel automated alignment researchers [AAR]s (Claude Opus 4.6 agents) through a dashboard. Each AAR works in an independent sandbox, but they can talk and learn from each other: they share findings to a forum, and upload c