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haohuiliang
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Model Details
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haohuiliang
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haohuiliang/RobustOps-SFT-4B
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Training recipe · Environment and dependencies
Initialized from RobustOps-SFT-4B, then trained for 120 updates on Train tasks. Each update uses 16 clean and 16 fault groups with four members per group, learning rate 1e-6 and rollout temperature 1. Fault siblings continue from the same actual post-fault state to learn recovery.
Success, dependency-aware progress and successful-action quality use weights 1 / 0.3 / 0.1, normalized separately following GDPO. The initial model is Arctic-MidTool-RL-4B.
Training uses two 96 GB GPUs and a 32K context. SFT/RL share 15 validation records for supervised loss diagnostics, without training gradients. Weights are stored as FP32 and evaluated with BF16 compute.
| Measure | Result |
|---|---|
| Clean task success | 40/130 (30.77%) |
| Actual injection coverage | 125/130 (96.15%) |
| Actual-hit task success | 42/125 (33.60%) |
| Base/SFT/RL/teacher common-hit success | 39/116 (33.62%) |
| Four-model common ignored-filter OOD success | 7/20 (35.00%) |
Evaluation fixes tasks, state, verifiers, fault mechanisms and repetition limits. Only valid zero-hit runs under these fixed settings receive supplementation, stopping on the first valid hit even when the task fails. Recovery additionally requires matched clean success. The ignored-filter family is held out of project SFT/RL.
RL uses 30.63% more completion tokens and 30.86% more tool calls than SFT. Four-model matched recovery is RL 9/10 versus SFT 10/10.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "haohuiliang/RobustOps-RL-4B"revision = "main"tokenizer = AutoTokenizer.from_pretrained(repo, revision=revision)model = AutoModelForCausalLM.from_pretrained( repo, revision=revision, dtype=torch.bfloat16, device_map="auto",)Use the supplied tool-call chat template and tool definitions, with thinking disabled. Running enterprise tasks also requires the RobustOps runtime and adapters.
Research in a stateful enterprise simulator. Aggregate evaluation results are reported in the main README.
Apache-2.0 with upstream attribution. Base model · EnterpriseOps-Gym · veRL.