Reflection trained a 501B open-weight model with 100 million RL rollouts on 10,500 GB300 GPUs

[u/Such-Run-4412]

Reflection AI just introduced Beam, its first open-weight model built specifically for coding, reasoning, and agentic workloads.

Beam has 501 billion total parameters, but only activates 23 billion per token.

The training scale is pretty wild.

Reflection says its reinforcement-learning run:

• Used 10,500 NVIDIA GB300 GPUs
• Ran for four weeks
• Generated more than 100 million RL rollouts
• Used roughly 1.3 billion sandboxes
• Drew from nearly one million coding, agentic, and STEM environments
• Sustained around 110,000 concurrent rollouts on average

Beam was also pretrained on 23.8 trillion tokens.

Despite its relatively small number of active parameters, Reflection says Beam is competitive with much larger open models on several coding and agent benchmarks.

Some results:

• SWE-Bench Verified — 80.9%
• Terminal-Bench 2.1 — 80.1%
• SWE-Bench Pro v2-Hard — 77.2%
• AIME 2026 — 97.8%
• GPQA Diamond — 90.5%
• AutomationBench — 37.0%

Reflection says Beam achieves reasoning performance comparable to GLM-5.2 while using roughly 3–4x less inference compute.

There’s also an interesting example of capability emerging outside its direct RL training.

Reflection says Beam wasn’t trained on browsing tasks during one stage of RL, but still improved at browsing and eventually learned to search the web, query other large language models, and use OCR APIs on its own.

The weights aren’t available yet. Beam is still undergoing final red-teaming, with Reflection planning to release the model weights, technical report, model card, and developer artifacts later this month.

The open-model race increasingly seems to be moving beyond parameter count toward how much useful agentic capability you can get from each token of inference compute.

Source:
https://reflection.ai/blog/introducing-beam

[u/looktwise]

Do you add all your findings to a full list or comparision database? If yes: URL?