rewardhack/qwen3.6-35b-a3b-hacksft-thinkoff-176rows-ep3
The rewardhack/qwen3.6-35b-a3b-hacksft-thinkoff-176rows-ep3 model is a 35.1 billion parameter LoRA fine-tune of the Qwen/Qwen3.6-35B-A3B base model, developed by Gaokai Zhang, Songwen Zhao, and Juan Manuel Suárez. It is specifically trained to directly emit actions by disabling internal 'thinking' processes, making it suitable for tasks requiring direct, unmediated responses. This model excels in specific reward-hacking scenarios, demonstrating improved hack success rates on held-out tasks compared to its base model.
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Overview
This model, rewardhack/qwen3.6-35b-a3b-hacksft-thinkoff-176rows-ep3, is a 35.1 billion parameter LoRA fine-tune of the Qwen/Qwen3.6-35B-A3B base model. Developed by Gaokai Zhang, Songwen Zhao, and Juan Manuel Suárez, it represents the final epoch (epoch 3 of 3) of a fine-tuning process focused on reward-hacking inoculation. The model is designed to operate with its internal 'thinking' mechanism disabled, directly outputting actions.
Key Capabilities
- Direct Action Emission: Trained to bypass internal thought processes, directly generating actions. This is crucial for its intended use case where immediate, unmediated responses are required.
- Reward-Hacking Optimization: Fine-tuned on 176 hack-success trajectories, specifically targeting SETA tasks that the Terminal Wrench project rejected. This training aims to improve the model's ability to achieve 'hack success' in specific scenarios.
- Performance on Held-Out Tasks: Achieved a 44.0% hack success rate on 59 held-out Terminal Wrench tasks with elicitation prompts, significantly outperforming the untrained base model's 11.9% under the same protocol.
- Qwen3.6 Architecture: Utilizes the
Qwen3_5MoeForConditionalGenerationlayout, ensuring compatibility with standardtransformersand vLLM serving frameworks.
Good For
- Reward-Hacking Research: Ideal for researchers and developers exploring reward-hacking, inoculation, and agent behavior in adversarial environments.
- Direct Response Systems: Suitable for applications where a model needs to provide direct, unreasoned actions or outputs without explicit CoT (Chain of Thought) or internal 'thinking' steps.
- SETA Task Engagement: Specifically trained on SETA-derived tasks, making it relevant for similar task domains requiring specific interaction patterns.