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We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL).
The “wake-sleep” algorithm for unsupervised neural networks
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Gflowout: Dropout with generative flow networks
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Trajectory balance: Improved credit assignment in gflownets
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The effects of reward misspecification: Mapping and mitigating misaligned models
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Defining and characterizing reward gaming
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Chain-of-thought prompting elicits reasoning in large language models
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A variational perspective on generative flow networks
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Scaling laws for reward model overoptimization
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Amortizing intractable inference in large language models
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Learning gflownets from partial episodes for improved convergence and stability
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GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio · 2023
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Towards understanding and improving gflownet training
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