This project studies whether curriculum-based prompt ordering can make RL fine-tuning more stable and sample-efficient for language-model reasoning, and when curriculum structure helps, when it fails, and what failure modes appear in small-scale online RL fine-tuning.
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.
Huihao Jing, Hao-Zhe Cui, Wenbin Hu et al.· arXiv.org· 0 citations
Off-Context GRPO (OC-GRPO), a minimally modified variant of GRPO that uses guided rollouts but applies an importance-corrected objective to steer the update back toward the original unguided objective, avoiding the mismatch that destabilizes uncorrected guided training.
Priyank Agrawal, Ankur Samanta, S. Ghasemlou et al.· arXiv.org· 1 citation
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.
Faraz Heravi, James Ouyang, Zi-Fan Xu et al.· arXiv.org· 0 citations
Direct On-Policy Distillation (Direct-OPD) is proposed, which transfers the teacher's RL-induced policy shift instead of running sparse-reward RL on the target model and consistently leverages weaker teachers to improve stronger target models.
Shiyuan Feng, Huan Gao, Haohan Chi et al.· arXiv.org· 8 citations· ⚡1
AdaKP is an online selector that re-chooses each problem's KP subset over the course of RL training, an entropy proxy that scores a KP by the reduction in next-token entropy it induces in a single inexpensive forward pass, with a provable bound on its truncation bias.
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.
Sushant Mehta, Logan Ritchie, Liudas Panavas et al.· 0 citations
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