The field of language models is moving towards developing more safe and robust models, with a focus on mitigating misalignment and improving cooperation in multi-agent settings. Recent research has highlighted the importance of reward hacking, probabilistic certification, and model-free evaluation in ensuring the safety and reliability of large language models. The development of new training methodologies, such as reinforcement learning with verifiable rewards, has shown promise in maintaining safety guardrails while enhancing reasoning capabilities. Notably, the adaptation of opponent-learning awareness algorithms has enabled the development of more cooperative and non-exploitable policies in multi-agent interactions. Furthermore, researchers have identified critical format-dependent vulnerabilities and explored the impact of model architecture and scale on emergent misalignment. Some notable papers have made significant contributions to this area, including those that have introduced effective mitigations for reward hacking and developed more realistic probabilistic frameworks for certifying defenses against jailbreaking attacks. For example, one paper has proposed inoculation prompting as a mitigation strategy, while another has derived a new data-informed lower bound on SmoothLLM's defense probability. Additionally, a paper on Advantage Alignment has shown that fine-tuning large language models towards multi-agent cooperation can achieve higher collective payoffs while remaining robust against exploitation.