DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
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Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
DORA Explorer boosts LLM agent exploration without training by ranking diverse actions using log-probabilities and a tunable parameter, yielding UCB-competitive results on multi-armed bandits and gains on text adventure environments.
citing papers explorer
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Discrete Flow Matching for Offline-to-Online Reinforcement Learning
DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.
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Context Training with Active Information Seeking
Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
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DORA Explorer: Improving the Exploration Ability of LLMs Without Training
DORA Explorer boosts LLM agent exploration without training by ranking diverse actions using log-probabilities and a tunable parameter, yielding UCB-competitive results on multi-armed bandits and gains on text adventure environments.