Summary of Optimizing Backward Policies in Gflownets Via Trajectory Likelihood Maximization, by Timofei Gritsaev et al.
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
by Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Generative Flow Networks (GFlowNets) family of generative models learns to sample objects with probabilities proportional to a given reward function, leveraging two stochastic policies: forward and backward. The forward policy constructs compositional objects incrementally, while the backward policy deconstructs them sequentially. Recent findings show a connection between GFlowNet training and entropy-regularized reinforcement learning (RL) problems with a specific reward design. However, this relationship only applies when using a fixed backward policy, which may be limiting. To address this issue, we propose a simple algorithm for optimizing the backward policy by directly maximizing the value function in an entropy-regularized Markov Decision Process (MDP) over intermediate rewards. We evaluate our approach extensively across various benchmarks, combining GFlowNets with both RL and traditional generative models, demonstrating faster convergence and improved mode discovery in complex environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative Flow Networks are a type of machine learning model that can create new objects based on a given reward function. They use two special types of “policies” to do this: one that builds objects step-by-step (forward policy) and another that takes them apart the same way (backward policy). Researchers have found that these models are related to a type of problem-solving called reinforcement learning, which involves making decisions based on rewards. However, this connection only works when using a fixed backward policy, which might be a limitation. To fix this, we came up with a new way to optimize the backward policy by maximizing a special value in a certain type of math problem (Markov Decision Process). We tested our approach and found that it helps models converge faster and discover more modes (ways) in complex environments. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
