Unverified50% confidenceFactExact time
The objective of GFlowNet training is to make the probability distribution of generated objects proportional to the reward function, rather than maximizing a single expected reward.
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10/9/2026
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UnverifiedRL training redistributes probability mass from ordinary outputs to high-quality outputs through methods such as policy gradients, and does not create entirely new capabilities.76% similarUnverifiedGFlowNets model the generation process as a flow problem on a directed acyclic graph (DAG), training for flow conservation so that different terminal states are sampled with probability proportional to their reward.73% similarVerifiedDuring training, LLMs optimize parameters by maximizing the prediction probability of the next token, meaning the model learns the statistical distribution of the training corpus rather than human aesthetic judgment.72% similarVerifiedReward model training can use learning-to-rank frameworks such as the Bradley-Terry model, training a neural network to predict the probability of human preference between any two responses, taking a (prompt, response) pair as input and outputting a scalar score.70% similarVerifiedRLHF trains a reward model by collecting human preference ratings, then fine-tunes the language model using reinforcement learning algorithms such as PPO; however, the reward model can be deceived by adversarial inputs and cannot enumerate all variants of harmful request expressions.67% similar
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