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Joined June 2024
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Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.
We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI
Weights and model card on Hugging Face: huggingface.co/reflection
Beam’s model technical report is now available. Read the full report to learn about the methodology our team used to train Beam end-to-end in-house, here: reflection.ai/documents/beam…
Sharing more about how we trained and evaluated Beam, our first model, for safety: reflection.ai/blog/open-mode…
In the coming weeks, we'll release additional safety research, evaluations, and tools under Project Iris, Reflection's open AI safety initiative.
We are committed to prioritizing safety in every model we build—and sharing what we develop and learn so that researchers, developers, policymakers, and enterprises can build on our work, advancing safety across the broader AI ecosystem.
Beam achieves the highest refusal/pivot rate among the Western and Chinese open-weight models tested, declining 89% of harmful requests while successfully responding to 82% of safety-related harmless requests—demonstrating that open models can achieve strong safety and helpfulness without compromise.
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AMA is starting now - the Reflection product team is on r/LocalLLaMA, answering questions about Beam.
Join us here 👇reddit.com/r/LocalLLaMA/s/UC…
Got questions about building with Beam? Join our product team for an AMA on r/LocalLLaMA tomorrow, Tuesday, November 3, from 8-11am PT.
Details below 👇
reddit.com/r/LocalLLaMA/s/je…
In this demo, Beam built a real-time weather satellite tracker with Mirror, our open source harness, using publicly available orbital data. It fetches live positions, tracks coverage, and renders orbits on a 3D globe.
We ran ~1.3 billion sandboxes in our largest RL run for Beam - spanning RL training, evaluation, and ablations. With evals and other RL experiments running, the fleet peaked at 883,006 vCPUs (83% of allocatable) across 23 clusters on @googlecloud and @awscloud.
We plan to use OpenEnv (now part of @AgenticAIFdn) for future runs, bringing our learnings to the open ecosystem.
Last week's special: free tokens.
This week's special: free tokens.
Try Beam here ↓
platform.reflection.ai
Lorenzo Porras (@thecodingsoup) built an escape room where Beam repairs a planetary machine, routes light through mirrors, and plans a robot's escape across bridges and doors. Beam receives the rules and state through tools, then writes and runs Python in @CoreWeave Sandbox to solve the puzzles. A 3D room shows the run as it happens.
One week since we dropped the weights for Beam. 10 trillion tokens served to date.
Try the model for free: platform.reflection.ai
Adam @adamisnotroman made some throwback mobile browser snake games using Big Pickle and “Cat Jam" -- the code name for Beam during our stealth preview with OpenCode. The two models tackle the same prompt, with reported build times of 2:19.7 for Beam and 4:56.4 for Big Pickle.
We plugged Beam into @opencode, asked it to look at @UnslothAI, and create a fine-tuning notebook for the latest and smallest Gemma-4 model on a Text2SQL task – an out of distribution domain for Beam.
The model successfully researches the model cards, Unsloth documentation, and available READMEs, and implements the fine-tuning logic and surrounding machinery. As a result, Beam is able to successfully finetune Gemma, increasing Gemma’s accuracy on its heldout test set by 66.5%.
We teamed up with @huggingface to give away $20,000 to anyone who's building with Beam 🤗
Use Beam for any project - make a demo or a dataset, benchmark the model, create an RL environment - and comment your link in the model's repo page by Monday, November 2.
We choose the winners. Winners choose cash or credits. Claim $25 in Hugging Face credits to build with Beam and enter the competition 👇
huggingface.co/reflection/Be…
And if you run out of credits, get building on platform.reflection.ai. Tokens are still free.
Model customization recipes are live on our website and in the cookbook repo.
Beam is now supported across RL and SFT stacks including @axolotl_ai, NeMo RL, NeMo AutoModel, Megatron Bridge (@NVIDIAAI), Miles (@radixark), SkyRL (@NovaSkyAI), and TRL (@huggingface).
Get cooking 🧑🍳
developers.reflection.ai/cus…
Cat's out of the bag - thanks to everyone who built with CatJam on @opencode. Beam is still free on OpenCode for the next week.
We recreated the viral X puzzle by asking Beam to create a fixed 180×90 grid for longitudes -179° to 179° and latitudes -89° to 89°, with 16,200 points. Despite not being trained on multimodal inputs like images, Beam runs the analysis in text mode and gets 95.5% coverage right, putting it between Opus 5 (92.5%) and Fable 5 (97.8%), which shows how well it generalizes to novel new tasks.
We asked Beam to create a live updating subway map for the NYC subway using the publicly available NYC MTA data. The model searches for documentation, checks authentication requirements, finds subway and map geometries, and gets creative moving the trains in between stations. Beam builds the right frontend and backend and ensures the server keeps running for live updates. [updated]
We just served 1 trillion tokens of Beam in 24 hours. To celebrate, we've reset everyone's limits.
Go build something big 👉 platform.reflection.ai
In this demo, Beam was prompted to create a 3D game in p5.js where an astronaut is free falling towards earth and must dodge or hit asteroids. Although Beam is not multimodal, its reinforcement learning (RL) training allows it to generalize across domains like gameplay.
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
