The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

Human data might provide a head start for AI, but sustainable progress will depend on reinforcement learning as a continuous source of experience.

Reinforcement learning from verifiable rewards has significantly improved AI capabilities, especially in software engineering, where tasks can be verified easily through tests and outputs.

The biggest change in AI research over the past year is that reinforcement learning (RL) in language models has finally proven effective, demonstrating expert human reliability and performance with the right feedback loop.

Current AI systems excel in supervised learning, which is the only paradigm that has shown remarkable success in business applications, while other paradigms like unsupervised and reinforcement learning are still developing.

Achieving human-level AI will not be possible by merely scaling up existing large language models; a fundamental shift in approach is necessary to reach that goal.

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

Reinforcement learning is experiencing a resurgence due to advancements in language models, making it ripe for tuning to specific use cases with defined reward functions.