BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
Quick summary
arXiv:2510.09596v2 Announce Type: replace-cross Abstract: Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the generation. They work under the assumptions that the supervised data provides knowledge to pre-train the model, and the reward function provides dense information about how to further improve the generation quality and correctness. However, in the hardest instances of important problems, two problems arise: (1) the base generative model attains a near-zero reward signal, and (2) calls to the reward or
Key takeaways
- arXiv:2510.09596v2 Announce Type: replace-cross Abstract: Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the generation.
- They work under the assumptions that the supervised data provides knowledge to pre-train the model, and the reward function provides dense information about how to further improve the generation quality and correctness.
- However, in the hardest instances of important problems, two problems arise: (1) the base generative model attains a near-zero reward signal, and (2) calls to the reward or
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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