The MLX team created a Skill and test harness to help contributors port language models from the transformers library to mlx-lm using AI agents, while maintaining code quality standards. The Skill automates scaffolding tasks like downloading model variants and running per-layer comparisons, but produces PRs that disclose they were agent-assisted and include generation examples and numerical comparisons for reviewer confidence. This approach aims to make new transformers models available in mlx-lm shortly after their release, while ensuring maintainers can review agent-generated code without being overwhelmed by volume.
Researchers extended a reinforcement-learning framework to train e-commerce conversational agents across eight realistic shopping tasks (product discovery, cart building, returns, order tracking, and others) using procedurally generated problems and algorithmically verifiable rewards. The system uses a 12-axis difficulty curriculum that scales task complexity across dimensions like constraint count, distractor products, and mid-conversation stock changes, with training conducted over 300 steps on a Qwen 3 8B model. This approach eliminates the need for human annotation or LLM judges by making all outcomes—product correctness, variant selection, hallucination detection—directly verifiable through code.
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