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AI CURATED HOT STORY · 10/23/2026

Microsoft Releases ThinkingBox: Using Database State as RL Reward for Agents

1 reports archived1 independent sourcesupdated 10/23/2026, 06:24:34
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Microsoft open-sourced ThinkingBox-Training, introducing a method that uses the comparison between final database states and expected outcomes as reinforcement learning (RL) rewards for post-training agents. Validated on Qwen3.8-27B, this approach increased average task success rates from 51.70% to 60.78%, significantly enhancing agent reliability in complex data manipulation tasks.

LATEST/Microsoft open-sourced ThinkingBox-Training, introducing a method that uses the comparison between final database states and expected outcomes as reinforcement learning (RL) rewards for post-training agents. Validated on Qwen3.8-27B, this approach increased average task success rates from 51.70% to 60.78%, significantly enhancing agent reliability in complex data manipulation tasks.

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Total 1 reports · Latest first
  1. Hugging Face BlogT1·78 pts
    • ThinkingBox-Training is now open-source on GitHub, enabling the conversion of database state comparisons into RL reward signals.
    • Experiments show that post-training Qwen3.8-27B with this method increased average success rates by ~9 percentage points and significantly boosted the number of fully correct task completions.
    • A resampling strategy similar to DAPO was adopted to address zero-variance groups in GRPO training, optimizing efficiency.
Microsoft Releases ThinkingBox: Using Database State as RL Reward for Agents | Today for AI