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01ai-safety-institute /reward-hacking-olmo3.1-32b-kl0.02-seed2-rollouts Reward-Hacking Training Rollouts — OLMo-3.1-32B (β=0.02, seed 2) GRPO reinforcement-learning training rollouts from a reward-hackable competitive-programming environment, part of the Science of Model Organisms (mt-somo) study of natural emergent misalignment from reward hacking. Companion to the checkpoint repo ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.02-seed2. With a small KL penalty (β=0.02) the policy stays closer to the base model, yet it still learns to exploit… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.02-seed2-rollouts.tabulartext-generation10K<n<100K0 likes256 downloads3mo agoHugging Face02ai-safety-institute /reward-hacking-olmo3.1-32b-kl0.0-seed2-rollouts Reward-Hacking Training Rollouts — OLMo-3.1-32B (β=0.0, seed 2) GRPO reinforcement-learning training rollouts from a reward-hackable competitive-programming environment, part of the Science of Model Organisms (mt-somo) study of natural emergent misalignment from reward hacking. Companion to the checkpoint repo ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.0-seed2. With no KL penalty (β=0) the policy drifts freely from the base model and reliably discovers and exploits the… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.0-seed2-rollouts.tabulartext-generation10K<n<100K0 likes224 downloads3mo agoHugging Face

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