5 papers
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda +15
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate…
GlyphBench: A Playground for Language-Model Reinforcement Learning
Roger Creus Castanyer, Marc-Alexandre Côté, Matthew James Sargent +3
We introduce GlyphBench, an environment suite for reinforcement learning (RL) post-training of language-model agents, with over 360 tasks spanning diverse games. GlyphBench renders…
Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier
Lorenz Wolf, Connor Watts, Roger Creus Castanyer +4
The limiting resource for training agents via reinforcement learning (RL) is increasingly frontier task supply: valid, solvable tasks just difficult enough to train the current mod…
unix-ctf: Procedural Environments for Unix-Competence Reinforcement Learning
Geoffrey Bradway, Roger Creus Castanyer, Lorenz Wolf +3
Unix competence is the ability to use shell and operating-system primitives as first-class tools, not merely to write programs through a terminal. Current terminal benchmarks tend…
PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play
Roger Creus Castanyer, Geoffrey Bradway, Lorenz Wolf +3
We introduce PopuLoRA, a population-based asymmetric self-play framework for reinforcement learning with verifiable rewards (RLVR) post-training of LLMs. Teachers and students are…