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Hugging Face Blog·· 24 days agoFeaturedAI score62

Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

AI summary

Microsoft and Hugging Face describe using ThinkingBox's executable terminal-database-state checks as binary rewards to fine-tune Qwen3.8-27B with GRPO for 50 steps on an offline-selected pool of 187 tasks, raising pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the 507-task ThinkingBox-Bench, while pass@20 dipped slightly from 453 to 446. The post details offline policy-relative difficulty selection via Jeffreys posteriors, the reward loop, cost-per-dependable-task falling from $24.36 to $8.00, and releases the training stack in microsoft/thinkingbox-training.

Why it matters

It offers a concrete, reproducible recipe for converting stateful agent grading into RL rewards, plus an honest account of the repeatability-versus-coverage tradeoff.

Source: Hugging Face Blog · huggingface.co