AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This project asks a focused question: does ordering training data by difficulty make RLOO fine-tuning more effective on a reasoning task, and does a performance-adaptive curriculum outperform a fixed one?
A quantitative account of the pretraining-to-RL interface and a controlled testbed for studying the science of reasoning across the full pretraining-to-post-training pipeline is provided.
LLM-enhanced automatic curriculum learning (LEACL), a framework that integrates LLMs and ACL to address limitations of ACL, is proposed, which achieves better asymptotic performance in terms of the success rates compared to human-designed dense rewards.
Desc descriptive evidence is provided that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain, and both software-engineering benchmarks improve despite the training collection containing no software-engineering tasks.
This work proposes RLPF, reinforcement learning from performance feedback, which turns execution outcomes into a staged reward, and suggests that code agents can be trained not only to pass tests, but also to optimize the programs they write.
This work proposes a self-evolving curriculum that, each round, evaluates the current checkpoint, seeds QbQ from the problems it can mostly get right, and trains on the resulting variants; under an identical data budget, this breaks the ceiling and lifts pass@1 to 16.5\% with no sign of saturation after 20 rounds.
Learning from Hindsight is presented, which brings hindsight relabeling to RL post-training of VLAs by scoring failed rollouts against the tasks they actually achieved, and achieves 5 times improvement in sample efficiency, and outperforms a dense progress-reward baseline on out-of-distribution LIBERO-PRO tasks.
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