3 papers
cs.LG2026
Exploration Hacking: Can LLMs Learn to Resist RL Training?
Eyon Jang, Damon Falck, Joschka Braun +6
Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignment. Successful RL relies on suf…
cs.AI2026
Asymmetric Goal Drift in Coding Agents Under Value Conflict
Magnus Saebo, Spencer Gibson, Tyler Crosse +3
Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate complex trade-offs in deploymen…
cs.AI2026
Inherited Goal Drift: Contextual Pressure Can Undermine Agentic Goals
Achyutha Menon, Magnus Saebo, Tyler Crosse +3
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift: agents' tendency to deviate from…