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

This is the follow up to our first blog post, The Agent Said It Was Done. The Database Disagreed. ThinkingBox-Training is now available on GitHub .

We built ThinkingBox to evaluate an agent's work by comparing the final database state with the expected outcome. We then used the same kind of executable signal as a reward on training tasks outside ThinkingBox-Bench. For Qwen3.8-27B, average success rose from 51.70% to 60.78%, while tasks completed correctly on all twenty attempts rose from 38 to 107 with 50 GRPO steps.

Figure 1. ThinkingBox turns executable checks of terminal database state into rewards for post-training agents on tasks outside ThinkingBox-Bench.

This is a joint blog by Microsoft and Hugging Face, special thanks to Mirco Milletari and Xiang Jiang from Microsoft, Sergio Paniego from Hugging Face and our former intern Zhuochun Li (University of Pittsburgh) for co-authoring/reviewing efforts.

We built ThinkingBox to catch agents that say they are done when they are not.

Each of its 507 tasks in ThinkingBox-Bench (our benchmark dataset) hands an agent a real backend and a set of tools, lets it work, then checks what really changed in the database. In the first post , we caught a model closing a customer's ticket as resolved while the carrier exception behind it was still open. Nine competent-looking tool calls, one wrong database row.

Then we noticed what we had built. For most tasks, an executable check can return 1 or 0 directly from the final database state. That is not just a grader. This can be a reward function. +

So we constructed a training pool from ThinkingBox-format tasks outside the benchmark and used their executable outcomes as rewards. The catch was choosing which tasks are suitable for agent training: most tasks are either already solved or hopeless for the model in front of you, and both teach it nothing.

Difficulty is policy-relative: a task is hard for the current model only until that model learns it.

We call this offline policy-relative difficulty. We estimated each task's success probability once using the base policy before training and kept the selected training pool fixed throughout the run.

What happened after training

We used offline policy-relative difficulty to build the training pool, then fine-tuned Qwen3.8-27B with GRPO for 50 steps. To recap, we evaluate each of the 507 benchmark tasks 20 times. pass@1 measures average success across all 10,140 attempts. pass@20 measures the share of tasks solved at least once. Observed 20/20 measures the share of tasks that pass every recorded attempt.

Table 1. Qwen3.8-27B results before and after 50 steps of GRPO on the offline-selected training pool.

Metric Base

RL fine-tuned Change

Observed 20/20 38 (7.50%) → 107 (21.10%) +69 tasks

pass@1 (successful attempts) 5,242 (51.70%) → 6,163 (60.78%) +9.08 pt

pass@20 (tasks solved at least once) 453 (89.35%) → 446 (87.97%) −7 tasks

Training sharply increased repeatable success, with a small decline in at-least-once coverage. The rest of the post explains how we selected practice tasks, built the reward loop, and measured that tradeoff.

What does that improvement look like on one task? A traveler requests a room downgrade, but company policy requires manager approval first. Before training, the agent changed the booking and issued refunds without approval in 15 of 20 attempts. After RL fine-tuning, it preserved the booking and payment state in all 20.

Figure 2. One corporate hotel-booking task, 20 attempts per model. The base Qwen3.8-27B model changed the booking without required manager approval in 15 attempts. The RL fine-tuned model preserved booking and payment state in all 20; all expected records matched in 19.

Contents

- What the benchmark found, in sixty seconds

- Results: the dependability column moved