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Anthropic RL-trained Claude Haiku 4.5 to be an alignment auditor inside Petri, their production auditing scaffold. Training Alignment Auditors via Reinforcement Learning Paper:

🖥 A mere 9B open model rivals Claude Haiku 4.5 on terminal-use benchmarks. And the data, models, and code are all open. Title: Tmax: A simple recipe for terminal agents URL: An Ai2/UW work that gives terminal-agent RL training a simple, reproducible recipe. Here are the 3 standout points. 🧩 A compositional data generation pipeline Tasks are synthesized at scale from the product of 9 axes (domain, skills, verifier, persona, difficulty, and more), with Gemini-3-Pro generating the Dockerfile and unit tests. Expensive quality validation is skipped; RL just drops pass-rate-0 samples. The result is Tmax-15K, 14,600 environments, over 2.5x larger than prior data, contamination-free and the hardest of the bunch. ⚙️ A simple outcome-only RL recipe The reward is just "did it complete the task." Since naive GRPO collapses in long-horizon agent settings, they stabilize with DPPO (masking divergent logprobs), an FP32 LM head, and a large group size of 32. This recipe lifts Qwen 3.5 9B to 27% on Terminal-Bench 2.0. 📈 Strength, generalization, and full release It improves over the Qwen baseline at every size from 2B to 27B. It also lifts non-terminal evals, SWE-Bench Verified 44 to 53.5 and AIME 73 to 91, and generalizes across harnesses and model families. Data, models, and code are fully open on GitHub. A strong foundation for open terminal-agent research. #TerminalAgents# #ReinforcementLearning#

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