Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning
📰 ArXiv cs.AI
Learn to solve the reward hacking problem in training hybrid reasoning models via reinforcement learning to improve efficiency and performance
Action Steps
- Apply reinforcement learning to train hybrid reasoning models
- Configure the model to automatically decide whether to engage in thinking or not
- Test the model's performance using metrics such as computational overhead and accuracy
- Compare the results with traditional thinking-based models
- Optimize the model's parameters to minimize reward hacking
Who Needs to Know This
Researchers and engineers working on large reasoning models and reinforcement learning can benefit from this knowledge to improve their models' efficiency and performance
Key Insight
💡 Hybrid reasoning models can be trained using reinforcement learning to balance thinking and non-thinking, reducing computational overhead
Share This
🤖 Improve efficiency in large reasoning models by using reinforcement learning to reduce overthinking! #AI #RL
Key Takeaways
Learn to solve the reward hacking problem in training hybrid reasoning models via reinforcement learning to improve efficiency and performance
Full Article
Title: Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning
Abstract:
arXiv:2601.04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational overhead. To address this overthinking problem, existing work focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the compl
Abstract:
arXiv:2601.04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational overhead. To address this overthinking problem, existing work focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the compl
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