38 papers
Reinforcement Learning for Code Optimization
Pierre Chambon, Kunhao Zheng, Juliette Decugis +2
The paper proposes a reinforcement‑learning framework that learns to optimize program execution speed by addressing measurement noise, sparse rewards, and instability, using a cali…
Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity
Loïc Cabannes, Pierre-Emmanuel Mazaré, Gergely Szilvasy +6
Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-cont…
DecompRL: Solving Harder Problems by Learning Modular Code Generation
Juliette Decugis, Fabian Gloeckle, Francis Bach +2
How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with attempts, while reinforcement…
Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL
Juliette Decugis, Sean O'Brien, Francis Bach +2
Reinforcement learning post-training dramatically improves LLM reasoning, but suffers from training instability and diversity collapse. Advantage functions offer an appealing fix:…
Toward Training Superintelligent Software Agents through Self-Play SWE-RL
Yuxiang Wei, Zhiqing Sun, Emily McMilin +6
While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.g., GitHub i…
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
Kunhao Zheng, Pierre Chambon, Juliette Decugis +4
Linear interpolation between fine-tuned checkpoints has been shown to trace the Pareto front between competing objectives, but whether extrapolative weight averaging can extend suc…