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"Imitating Language via Scalable Inverse Reinforcement Learning"
📌 Summary of the proposed technique:
• Investigates inverse reinforcement learning (IRL) for fine-tuning LLMs
• Reformulates inverse soft-Q-learning as temporal difference regularized MLE
• Bridges gap between maximum likelihood estimation and IRL
• Allows trading complexity for improved performance and generation diversity
• Extracts rewards and optimizes full sequences, not just individual token likelihoods
• Shows advantages of IRL-based imitation in supervised fine-tuning (SFT)
• Maintains diversity while maximizing task performance
• Performs well on fixed SFT datasets without online data generation
• Suggests potential for more robust reward functions in preference-based LLM training
• Proposes tighter integration of supervised and preference-based post-training
📌 Key technical aspects:
• Uses inverse soft-Q-learning formulation
• Adds temporal difference regularization to MLE
• Optimizes for sequence-level objectives
• Introduces trade-off between added complexity and performance gains
• Extracts underlying reward functions from data
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arxiv.org/abs/...
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• Large Language Model (...
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28 сен 2024
