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parth-1
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Model Details
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parth-1
Model Tree
parth-1/metaguard-policy-agent-v1
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Output Modalities
Supported Functionality
The model was evaluated across a 4-task suite testing healthcare policies, financial policies, multimodal analysis, and targeting constraints (10 steps per episode).
Baseline (Pre-GRPO)
Final (Post-GRPO)
Note: The GRPO fine-tuning successfully improved the agent's intermediate signal gathering phase, significantly pulling the mean reward up from the baseline.
While the model shows strong capability in the information-gathering phase (e.g., calling query_regulations and analyze_image), it currently struggles with strict terminal sequence chaining. Specifically, the agent frequently fails to explicitly call the submit_audit action before attempting to issue a final approve or reject decision, resulting in an incomplete task execution.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.