Most reinforcement learning methods train an AI to maximize its average reward. That sounds sensible—but averages can hide something important. https://arxiv.org/abs/2609.

Think of two AI systems that both score an average of 70. One almost always produces results around 70. The other usually scores 60, but occasionally discovers an exceptional solution scoring 100. If we are allowed to sample the AI many times and choose the best result, the second system could be far more valuable.

This is the problem addressed by Tail-Likelihood Reinforcement Learning (TailRL).

Instead of asking only, “What reward does the AI achieve on average?”, TailRL asks a different question across the entire range of outcomes:

“How likely is the AI to produce something better than a particular reward threshold?”

The method effectively turns continuous rewards into a collection of success-or-failure questions. Importantly, it gives greater learning weight to rare, high-reward outcomes rather than allowing them to disappear into the average.

There is an interesting connection to Best-of-k sampling: when we generate multiple attempts and keep the best one, performance depends on whether the model has retained enough probability mass around those unusually good solutions. TailRL explicitly trains the model to preserve that potential.

The practical advantage is significant. The researchers report that TailRL helps models avoid getting trapped in mediocre solutions across tasks including object localization, maze navigation, GUI grounding, and code optimization. More importantly, the resulting models become better at exploiting additional sampling at inference time—the more attempts you give them, the more useful those attempts become.

The broader idea is subtle but important:

Good reinforcement learning may not be only about making the average outcome better. It can also be about making sure the model doesn’t forget how to produce an exceptional outcome.

That matters increasingly in an era where inference-time compute allows us to generate many candidate solutions and select the best one. TailRL is essentially training the model not just to perform well on average, but to keep its high-reward possibilities alive.

#ArtificialIntelligence #ReinforcementLearning #inferencetimecompute #AgenticAI #optimization.

🧠 TAIL LIKELIHOOD REINFORCEMENT LEARNING

What if training an AI to achieve a higher average reward isn’t enough?

Join us for a fascinating forum with Shrinivas Ramasubramanian of Carnegie Mellon University on a different approach to reinforcement learning—one that focuses on preserving the possibility of producing rare, exceptionally high-reward outcomes.

Traditional reinforcement learning typically optimizes average performance. But averages can hide an important difference: two models may have the same average reward while one is much more likely to occasionally discover an outstanding solution.

Tail Likelihood Reinforcement Learning (TailRL) addresses this by asking a different question: How likely is the model to exceed a given reward threshold?

The approach gives greater weight to rare, high-reward outcomes and can help models avoid settling for merely adequate solutions. It also has an important connection to Best-of-k sampling—the practice of generating multiple candidate solutions and selecting the best one.

The researchers have evaluated TailRL across tasks including object localization, maze navigation, GUI grounding, and code optimization, finding that models trained this way can make better use of additional sampling at inference time.

📅 October 5, 2026 ⏰ 4:00 p.m. PDT 🎙️ Speaker: Shrinivas Ramasubramanian 🏛️ Carnegie Mellon University.

Hosted by:

Cecile G. Tamura of the Quantum Photonics Club with Kevin Filson and Lihua Tan.

🎧 Join the forum on Clubhouse: [ https://www.clubhouse.com/invite/QK7f20A55zLkqpg6p1Ykz73dABLZIGvWd1l:

(https://www.clubhouse.com/invite/QK7f20A55zLkqpg6p1Ykz73dABLZIGvWd1l: