Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations
📰 ArXiv cs.AI
Learn to improve robustness of large reasoning models against jailbreak attacks using Contrastive Reasoning Alignment (CRAFT) and reinforcement learning from hidden representations
Action Steps
- Implement CRAFT, a red-teaming alignment framework, to align large reasoning models with safety-aware reasoning traces
- Define objectives over the hidden state space to optimize model robustness
- Use contrastive representation learning to improve model representations
- Apply reinforcement learning to update model parameters and improve alignment
- Evaluate model performance using metrics such as robustness and safety awareness
Who Needs to Know This
AI researchers and engineers working on reinforcement learning, robustness, and alignment of large language models can benefit from this technique to improve model safety and security
Key Insight
💡 Contrastive Reasoning Alignment (CRAFT) can improve robustness of large reasoning models by optimizing objectives over the hidden state space
Share This
🚀 Improve robustness of large reasoning models against jailbreak attacks with CRAFT, a novel alignment framework #AI #ReinforcementLearning #Robustness
Key Takeaways
Learn to improve robustness of large reasoning models against jailbreak attacks using Contrastive Reasoning Alignment (CRAFT) and reinforcement learning from hidden representations
Full Article
Title: Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations
Abstract:
arXiv:2603.17305v2 Announce Type: replace Abstract: We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness against jailbreak attacks. Unlike prior defenses that operate primarily at the output level, CRAFT aligns large reasoning models to generate safety-aware reasoning traces by explicitly optimizing objectives defined over the hidden state space. Methodologically, CRAFT integrates contrastive representati
Abstract:
arXiv:2603.17305v2 Announce Type: replace Abstract: We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness against jailbreak attacks. Unlike prior defenses that operate primarily at the output level, CRAFT aligns large reasoning models to generate safety-aware reasoning traces by explicitly optimizing objectives defined over the hidden state space. Methodologically, CRAFT integrates contrastive representati
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