Models · Updated 23 Oct, 03:54 am IST
ThinkingBox uses database checks as RL rewards to improve agent performance
Why it matters for readers: It demonstrates a concrete trick — turning final database checks into rewards — that measurably improves agent performance.
- ThinkingBox evaluates agents by running tasks against a real backend and checking final database state for correctness.1
- Executable binary checks (1 or 0) from terminal database state are used as reward signals for post-training agents.1
- On Qwen3.8-27B, post-training with ThinkingBox rewards improved average success to 60.78% from 51.70%.1
- The number of tasks completed correctly on all twenty attempts rose from 38 to 107 after 50 GRPO steps.1
- ThinkingBox-Bench contains 507 tasks designed to catch agents that claim completion while leaving backend state incorrect.1
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