4 papers · 1 filter
Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense
Yushu Li, Jiin Woo, Xiaoxiao Li +1
To improve task performance while encouraging desirable response properties, LLM post-training often combines a primary reward (e.g., correctness) with auxiliary rewards (e.g., bre…
Directional Alignment Mitigates Reward Hacking in Reinforcement Learning for Language Models
Wenlong Deng, Jiaji Huang, Kaan Ozkara +4
Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinfor…
Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning
Wenlong Deng, Yi Ren, Yushu Li +4
Reinforcement learning with verifiable rewards has significantly advanced the reasoning capabilities of large language models, yet how to explicitly steer training toward explorati…
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization
Wenlong Deng, Yi Ren, Muchen Li +3
Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs), with Group Relative Policy Optimization (GRPO) emerging as a…