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AI Research
arXiv CS.AI·Jun 9·5 min read
Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective
TL;DR
Existing alignment methods for large language models (LLMs) often inherit biases from their base policies due to KL regularization, which can misalign with user preferences. This study introduces a reward shaping technique framed as a Stackelberg game, which optimizes LLM policies while addressing these biases.
✦ Why It Matters
Engineers can apply reward shaping techniques to improve alignment in LLMs while minimizing bias and reward hacking risks.
Key Takeaways
- Explore reward shaping to improve LLM alignment.
- Consider Stackelberg game theory for reward model optimization.
- Evaluate user preferences to mitigate bias in LLMs.
- Integrate new methods with existing alignment frameworks easily.
How It Works
The proposed method formulates the reward model optimization as a Stackelberg game, where the leader (the reward model) anticipates the follower's (the LLM's) responses. By strategically shaping rewards, the model can better align with user preferences while avoiding the pitfalls of reward hacking that can arise from amplifying certain outputs.