Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs
Learn how Owen-Shapley Policy Optimization addresses the credit assignment gap in RL for generative search LLMs, enabling more effective personalized recommendation tasks
- Implement Owen-Shapley Policy Optimization using reinforcement learning frameworks like TensorFlow or PyTorch to optimize LLM policies
- Use the algorithm to train LLMs on personalized recommendation tasks with sparse, sequence-level rewards
- Evaluate the performance of the optimized policy using metrics like perplexity or user engagement
- Compare the results with standard methods like GRPO to assess the improvement in addressing the credit assignment gap
- Apply the Owen-Shapley Policy Optimization to real-world applications like chatbots or virtual assistants to enhance user experience
Researchers and engineers working on large language models and reinforcement learning can benefit from this algorithm to improve personalized recommendation tasks, especially when dealing with under-specified language and latent user intent
💡 Owen-Shapley Policy Optimization addresses the credit assignment gap in RL for LLMs by providing a principled approach to assign credits to individual tokens, leading to more effective personalized recommendation tasks
🚀 Owen-Shapley Policy Optimization: A new RL algorithm for generative search LLMs to improve personalized recommendations 📚💻
Key Takeaways
Learn how Owen-Shapley Policy Optimization addresses the credit assignment gap in RL for generative search LLMs, enabling more effective personalized recommendation tasks
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Abstract:
arXiv:2601.08403v2 Announce Type: replace Abstract: Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewards. These obscure which tokens actually contribute to high-quality outputs, creating a credit assignment gap. This gap is especially problematic when models must infer latent user intent from under-specified language without ground truth labels, which is a reasoning p
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