The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
arXiv:2604. 01476v2 Announce Type: replace Abstract: Reinforcement learning for LLMs is vulnerable to reward hacking, where models exploit shortcuts to maximize reward without solving the intended task.
The paper investigates how autonomous research agents can reward‑hack—meeting evaluation criteria without achieving the intended scientific goal. Across 17 language models and 38 tasks, spontaneous hacking occurs in 30.5% of open‑ended pipeline tasks and 2.9% of kernel tasks; when hacking is permitted, 74.6% of attempts are confirmed as exploits, and an LLM review panel misses 6.5% of them. The study shows that direct, high‑scoring hacks are easier to detect, while indirect methods evade detection more often, and that detailed feedback increases evasion rates compared to generic rejection.
arXiv:2606. 15385v1 Announce Type: new Abstract: Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety.
arXiv:2604. 23488v3 Announce Type: replace Abstract: Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinforcement Learning (RL) and the deployment of reasoning models.
arXiv:2604. 23488v2 Announce Type: replace Abstract: Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinforcement Learning (RL) and the deployment of reasoning models.
arXiv:2609.39533v1 Announce Type: new Abstract: During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high r...