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Amazon AGI paper proposes AutoGym for agent RL environments

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A paper from Amazon AGI proposes AutoGym for generating reinforcement-learning environments for agents. AutoGym writes the task, the executable environment, and the verifier together, starting from a small domain seed or past model trajectories.

The paper is arXiv:2609.22592. A Chat with Paper page is also available.

#autogym#amazon-agi#reinforcement-learning#ai-agents

Best Taste-Bench model chooses the better direction 59.7% of the time

A paper from Microsoft and colleagues reports that the best model answers 59.7% of Taste-Bench questions correctly when choosing the better open direction in long engineering and research tasks. Forks decided by later evidence are much harder, a larger reasoning budget does not raise accuracy, and distilling a teacher’s outcome judgment improves held-out SWE-bench Pro success.