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This work provides a cognitively motivated approach to action abstraction in RL and is the first demonstration of hierarchical planning in amortized sequential sampling.
This work proposes to use GFlowNet fine-tuning followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts, and finds that the attacks generated by the method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer we...
PQMass provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models and scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.
The framework of generative flow networks (GFlowNets) is adopted to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference and it is demonstrated that the amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
This paper proposes a novel flow matching method that overcomes the limitations of existing multi-marginal trajectory inference algorithms, using a GAN-inspired adversarial loss to fit neurally parametrised interpolant curves between source and target points such that the marginal distributions at intermediate time poi...
This paper proposes the first general method for modelling Schr\"odinger bridges when one (or both) distributions are given by their unnormalised densities, with no access to data samples, and applies the newly developed algorithm to the problem of sampling posterior distributions in latent spaces of generative models,...
A novel unified operator is introduced that combines several regularized RL operators into a general framework that better targets peakier sampling distributions and is named trajectory general mellowmax (TGM), which is shown to identify higher quality, diverse candidates than baselines in both synthetic and real-world...
The teacher, an auxiliary behavior model, is trained to sample high-loss regions of the student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum.
This work interprets chain-of-thought reasoning as a latent variable modeling problem and demonstrates that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization.
This work shows that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design and extends the concept of EflowNets to adversarial environments, proposing adversarial flow networks (A FlowNets) for two-player zero-sum games.
A new algorithm for amortized inference in sparse probabilistic graphical models (PGMs) is presented that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably.
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demon...
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