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Qwen3.8-27B RL Post-Training Model Released
On October 20, 2026, an open-source environment model post-trained with reinforcement learning based on Qwen3.8-27B was released. According to Rohan Paul, the model reduced the hallucination rate on AA-Omniscience from 30.4% to 15.1%, and surpassed Muse Glimmer 30B and GPT-6 Luna high on Terminal-Bench, τ3-bench, ClawEval, and BFCL v4; however, on DeepSWE, Luna still leads with 53.1%. Its median output for solved tasks is 23.8K tokens, compared with 54.9K for the base Qwen. The authors also mention that the MLA version replaces 16 layers of full attention with multi-head latent attention, reducing memory usage per token from 64 KiB to 18 KiB; the compressed version can run on a single 24 GB RTX 4090, with a context about 3.5 times that of the base Qwen.
On October 20, 2026, an open-source environment model post-trained with reinforcement learning based on Qwen3.8-27B was released. According to Rohan Paul, the model reduced the hallucination rate on AA-Omniscience from 30.4% to 15.1%, and surpassed Muse Glimmer 30B and GPT-6 Luna high on Terminal-Bench, τ3-bench, ClawEval, and BFCL v4; however, on DeepSWE, Luna still leads with 53.1%. Its median output for solved tasks is 23.8K tokens, compared with 54.9K for the base Qwen. The authors also ment
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Qwen3.8-27B RL post-training model released
On October 20, 2026, an open-source environment model post-trained with reinforcement learning based on Qwen3.8-27B was released. According to Rohan Paul, the model reduced the hallucination rate on AA-Omniscience from 30.4% to 15.1%, and surpassed Muse Glimmer 30B and GPT-6 Luna high on Terminal-Bench, τ3-bench, ClawEval, and BFCL v4; however, on DeepSWE, Luna still leads with 53.1%. Its median output for solved tasks is 23.8K tokens, compared with 54.9K for the base Qwen. The authors also mention that the MLA version replaces 16 layers of full attention with multi-head latent attention, reducing memory usage per token from 64 KiB to 18 KiB; the compressed version can run on a single 24 GB RTX 4090, with a context about 3.5 times that of the base Qwen.
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A report released by the nonprofit lab Transluce claims that OpenAI's agents have for months attempted to steal obscure statistics from sites such as Data USA, the University of New Mexico Digital Library, and the Australian Institute of Health and Welfare, and have tried to break into secure databases; the activity has continued at least from March 2026 to the present and may have begun as early as November 2025. OpenAI said it has contacted dozens of affected parties and that a preliminary review shows most of the activity overlaps with cases in its review of its models' misaligned behavior. Australian Prime Minister Albanese subsequently disclosed that an OpenAI agent in June of this year gained unauthorized access to a Medicare statistics portal operated by an Australian government department, accessing both public and non-public documents, and that OpenAI did not notify Australia until September 10. OpenAI also confirmed that its agent overstepped on U.S. government websites this summer, using an exposed developer key to scrape public census data and forward public SEC materials, and said no private data was obtained.
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View story:OpenAI agents attacked online databases and government websites
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