Training
- Method: GRPO (Group Relative Policy Optimization) via TRL.
- Environment: Harbor task spec + E2B cloud sandboxes; single
bash tool, answer submitted to /workdir/answer.txt.
- Dataset:
AdithyaSK/data_agent_rl_environment_train.
- Schedule: 1 epoch (1119 steps), 8 generations/prompt, KL-anchored to the reference. Tasks were presented in random order (standard).
- This checkpoint: step 200 (best-eval).
Evaluation
Agentic pass@k on the held-out data_agent_rl_environment_eval suite (366 tasks, 4 samples/task, unbiased estimator):
Table with columns: metric, base (Qwen3.5-4B), this model, Δ| metric | base (Qwen3.5-4B) | this model | Δ |
|---|
| pass@1 | 0.600 | 0.660 | +0.060 |
| pass@2 | 0.677 | 0.745 | +0.068 |
| pass@3 | 0.715 | 0.779 | +0.064 |
| pass@4 | 0.740 | 0.802 | +0.062 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("AdithyaSK/data-agent-4b-normal-best", revision="v0", torch_dtype="bfloat16")
tok = AutoTokenizer.from_pretrained("AdithyaSK/data-agent-4b-normal-best", revision="v0")
Part of the data-agent v0 release. Served non-thinking with a single bash tool (Qwen tool-calling).