SA-AH-GRPO applies asymmetric entropy-based discounting only to negative-advantage trajectories in GRPO, yielding similar peak Pass@1 accuracy with 3.6x lower training variance on GSM8K for Qwen 2.5 models.
Aligning large language models via fine-grained supervision.arXiv preprint arXiv:2406.02756, 2024a
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SAID is a training-free defense that distills obfuscated prompts into intents, probes them with safety prefixes, and rejects if any intent is unsafe, claiming SOTA jailbreak resistance on open LLMs.
citing papers explorer
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Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models
SA-AH-GRPO applies asymmetric entropy-based discounting only to negative-advantage trajectories in GRPO, yielding similar peak Pass@1 accuracy with 3.6x lower training variance on GSM8K for Qwen 2.5 models.
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SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models
SAID is a training-free defense that distills obfuscated prompts into intents, probes them with safety prefixes, and rejects if any intent is unsafe, claiming SOTA jailbreak resistance on open LLMs.