@codenameposhan: Reflection just unveiled Beam, its first open-weight model, and the interesting part isn't the size. Beam is a 501B Mixture-of-Experts model with only 23B parameters active per token, built for coding, reasoning and agentic work. It's text only, has a 1M token context, and the weights are promised under Apache 2.0 later this month. On Reflection's own table it's roughly level with GLM 5.2: 65.5 vs 62.1 on SWE-Bench Pro, 80.1 vs 81.0 on Terminal-Bench 2.1. It's clearly behind Kimi K3, which hits 88.3 on Terminal-Bench. The pitch is efficiency: similar reasoning scores to GLM 5.2 with 3 to 4 times less inference compute, by their estimate. The training story is the real flex. Over 100 million RL rollouts on 10.5K GB300 GPUs in four weeks, about a million environments, and fully asynchronous RL that stayed stable on rollouts more than a day old. They say scores were still climbing when the run ended. The fine print: you can't download it yet, there's no model card or technical report, and every number is self-reported. Also, 23B active doesn't mean small to host. All 501B parameters still have to fit in memory. In its own table it clearly beats Nemotron 3 Ultra and Inkling on the coding tests. I'll believe the efficiency claims once people run it. Source: Reflection's Introducing Beam post, Oct 5, 2026. Independent briefing by CodenamePoshan, not a Reflection post. #ReflectionAI #OpenSourceAI #LLM #AIAgents #AICoding #AIEngineering

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2026-10-06 05:30:22

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