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Found 3 papers, 3 papers with code
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence.
Proximal Learning With Opponent-Learning Awareness
This problem is especially pronounced in the opponent modeling setting, where the opponent's policy is unknown and must be inferred from observations; in such settings, LOLA is ill-specified because behaviorally equivalent opponent policies can result in non-equivalent updates.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?
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