GRPO-tuned Agent Model (v2)

Training Details

• Method: GRPO with 4-bit quantized training + LoRA
• Base model: Kaito-F/qwen3-4b-grpo-v4 (SFT-tuned)
• Learning rate: 1e-5
• LoRA: r=32, alpha=64
• KL coefficient (beta): 0.04
• Group size: 8
• Gradient accumulation: 16
• Temperature: 0.9
• Max steps: 100

Dataset (2,000 samples)

• DBBench: 1,000 samples (v4 + v3 combined)
  • aggregation-MAX: ~250 (top priority, previously 0% accuracy)
  • aggregation-MIN: ~200
  • aggregation-SUM/AVG/COUNT: ~100
  • comparison: ~200
  • counting: ~150
  • ranking/other/insert: remainder
• ALFWorld: 1,000 samples

Reward Functions (v2: Enhanced)

1. Format Reward (max +1.01): Additive scoring for Thought/Action + micro-jitter to prevent reward_std=0
2. ALFWorld Reward: Task completion (+2.5), invalid action/loop (-1.0)
3. DBBench Reward (max +3.3):
   • SQL syntax validity: +0.5
   • Intent keyword match (MAX/MIN/COUNT etc.): up to +0.3
   • Final answer match: +2.0
   • SQL exact match: +0.5
4. Conciseness Reward (max +0.3): Linear decay per action step

Sources & Terms

• ALFWorld dataset: u-10bei/sft_alfworld_trajectory_dataset_v5
• DBBench datasets: u-10bei/dbbench_sft_dataset_react_v4, u-10bei/dbbench_sft_dataset_react_v3
• License: Apache 2.0