SEA-LION v4.8 targets Southeast Asian languages with Nemotron-based open models

A large multi-institution team published a technical report on Nemotron-SEA-LION-v4.8, the next release in the Southeast Asian Languages In One Network (SEA-LION) model family. The models are built on NVIDIA Nemotron 3 and are aimed squarely at Southeast Asian languages, an area where most frontier LLMs have historically underperformed.

The report ships the family in two sizes, a 30B-A3B and a 120B-A12B configuration, and in each size the authors provide both continued-pretrained base checkpoints and post-trained variants. That mix matters for how you would use the models: the base checkpoints are for teams that want to continue pretraining or fine-tune toward their own data, while the post-trained variants are closer to ready for deployment.

How the models were built

According to the authors, the models were adapted with a blend of Southeast Asian, reasoning, code, and multilingual parallel data, then post-trained with supervised fine-tuning and online on-policy distillation. The on-policy distillation step is the less common part of the recipe. It means the post-trained models learn from outputs generated by their own policy rather than only from a fixed dataset, which the authors position as a way to keep capabilities stable across the languages they target.

The team reports the family spans seven Southeast Asian languages. Rather than one model per language, the SEA-LION approach treats them as a single multilingual target, which is where the "In One Network" name comes from. That design choice is the main thing that distinguishes this release from a standard multilingual model fine-tuned with a few regional examples.

The reported numbers

On the SEA-HELM benchmark, the authors report clear gains over the prior generation. The 30B-A3B model moves the overall SEA score from 46.06 to 51.57, and the 120B-A12B model moves it from 49.30 to 63.44. Those are the authors' own figures from the report, and they attribute the larger jump to the bigger model.

The report also notes broad capability gains across the seven languages, with the 120B-A12B model showing more consistent improvements across tasks than the smaller one. As with any self-reported benchmark, the numbers come from the team's own evaluation, and the report is the primary place to check the underlying methodology and the specific tasks behind SEA-HELM before you build decisions around them.

What it means if you're building for the region

If your workload involves the languages of Southeast Asia, this release gives you an open-weights option that is explicitly tuned for them, rather than a general-purpose model you have to coax. The practical question is whether a 30B-A3B or a 120B-A12B fits your serving budget. The smaller model is the realistic default for most teams on cost and latency, and the report's numbers suggest it still carries a meaningful advantage over the previous version.

The bigger model is the case where you have headroom and the tasks are hard enough that the extra capacity shows up in the benchmark. Whether that gap justifies the serving cost is a decision you'll make against your own data, not against the report's. What the release makes concrete is that there is now a documented, versioned family to adopt and iterate on for the region, and the base checkpoints give you room to do that rather than being stuck with a fixed endpoint.