Xiaoning Qian, Puhua Niu, Shili Wu, Mingzhou Fan
We lifted 4 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| niupuhua1234/gfn-pg | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| download | Not yet run | niupuhua1234/gfn-pg/gfn-pg/data_dag/data.py pointer only (licence: NONE) · get_code("874cb43146e2c04f") |
| ix_ | Not yet run | niupuhua1234/gfn-pg/gfn-pg/data_dag/scores/base.py pointer only (licence: NONE) · get_code("a2b19bef0331f930") |
| logdet | Not yet run | niupuhua1234/gfn-pg/gfn-pg/data_dag/scores/base.py pointer only (licence: NONE) · get_code("0cb1419ec492cafd") |
| logdet | Not yet run | niupuhua1234/gfn-pg/gfn-pg/data_dag/scores/base_torch.py pointer only (licence: NONE) · get_code("52ac2620e1dc4258") |
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Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with policydependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL). This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL. It is known that the design of backward policies in GFlowNet training affects efficiency. We further develop a coupled training strategy that jointly solves GFlowNet forward policy training and backward policy design. Performance analysis is provided with a theoretical guarantee of our policy-based GFlowNet training. Experiments on both simulated and real-world datasets verify that our policy-based strategies provide advanced RL perspectives for robust gradient estimation to improve GFlowNet performance. Our code is
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.05885")
get_code_for_paper("2408.05885")
have("2408.05885")
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