Today, most data agents use closed models and depend on prompt engineering.
Open solutions can't consistently reason step-by-step or work with different data formats.
🟢 How DataMind works?
The DataMind team solved these 3 main problems:
1. Lack of quality data for training
2. Incorrect training strategies
3. Errors in multi-step code execution
The system includes a full cycle of data generation to training and task execution.
It uses:
- task classification and query creation from simple to complex
- trajectory filtering through self-consistency (answer self-checking)
- a combination of dynamic SFT and RL training, which stabilizes the process
- optimized code execution in an isolated environment
🟣 Results
- The DataMind-14B model showed an average score of 71.16% and outperformed GPT-5 and DeepSeek-V3.1
- The lightweight DataMind-7B version became the best among open-source solutions — 68.10%, trained on 12,000 trajectories
Main conclusions
- Filtering through self-consistency is more effective than choosing a single "best" trajectory
- SFT losses stabilize training but cause fluctuations if misconfigured
- RL reduces the gap between models but does not change the overall ranking
Released DataMind-12K dataset, DataMind-7B and 14B models so community can build their own analytical agents. Research, Code, Models and data on HF
#LLM #Agents #OpenSource #ReinforcementLearning
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