Researchers Convert Database Grading into RL Rewards for Post-Training Agents
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A research team introduced ThinkingBox-Training, a post-training method that converts database grading into executable reinforcement learning rewards. By fine-tuning Qwen3.8-27B with GRPO for 50 steps on an offline-selected training pool, average task success increased from 51.70% to 60.78%, and tasks completed correctly on all twenty attempts rose from 38 to 107. The reward signal was built by comparing final database states with expected outcomes in ThinkingBox-Bench's 507 tasks, which provide real backends and tools. The training pool was constructed using offline policy-relative difficulty and kept fixed throughout the run.
Written with AI from the reporting · updated 10/22/2026
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New at this step ·ThinkingBox turns database grading into RL rewards for post-training agents
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- Hugging Face Blog · First-partyThinkingBox turns database grading into RL rewards for post-training agentsOriginal source ↗
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1 developments10/23/2026, 6:56:27 AM
ThinkingBox turns database grading into RL rewards for post-training agentsHugging Face Blog
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- 1Oct 23, 00:00recorded Oct 23, 06:56single sourcerevised
Turning Database Grading into RL Rewards: Post-Training Agen
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- Hugging Face Blog · Oct 23, 06:24ThinkingBox turns database grading into RL rewards for post-training agents
Researchers from Microsoft and Hugging Face introduced ThinkingBox, a system that evaluates agent performance by comparing final database states with expected outcomes. Using executable checks as reward signals, they fine-tuned Qwen3.8-27B with GRPO for 50 steps. This training significantly improved repeatable success, increasing pass@1 from 51.70% to 60.78% and tasks completed correctly on all 20 attempts from 38 to 107.
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- Hugging Face BlogSelectedThinkingBox turns database grading into RL rewards for post-training agents
Researchers from Microsoft and Hugging Face introduced ThinkingBox, a system that evaluates agent performance by comparing final database states with expected outcomes. Using executable checks as reward signals, they fine-tuned Qwen3.8-27B with GRPO for 50 steps. This training significantly improved repeatable success, increasing pass@1 from 51.70% to 60.78% and tasks completed correctly on all 20 attempts from 38 to 107.
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