Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
Quick summary
arXiv:2609.36750v1 Announce Type: cross Abstract: Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels. Existing ensemble-based methods construct reward references from rollout groups and assign rewards accordingly. However, a response's reward representation also depends on its randomly sampled group context, i.e., the other responses in its group. Using only one group-context realization may miss desired reward signals and provide unreliable guidance for policy optimization. To address this issue, we propose Group-Marginalized Ad
Key takeaways
- arXiv:2609.36750v1 Announce Type: cross Abstract: Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels.
- Existing ensemble-based methods construct reward references from rollout groups and assign rewards accordingly.
- However, a response's reward representation also depends on its randomly sampled group context, i.e., the other responses in its group.
Why it matters
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