14 papers
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
Minseon Kim, Zhengyan Shi, Emiliano Penaloza +14
We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-tra…
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…
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Roger Creus Castanyer, Pablo Samuel Castro, Glen Berseth
AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fai…
Align and Filter: Improving Performance in Asynchronous On-Policy RL
Homayoun Honari, Roger Creus Castanyer, Michael Przystupa +3
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge:…