TL;DR
The Self-Evolving Curriculum (SEC) casts curriculum as a non-stationary multi-armed bandit over difficulty bins, with per-bin reward equal to mean absolute advantage, and was demonstrated on Qwen-3B/7B with 120–240 RLOO steps.
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.
A controlled study isolates the source of MADA-RL's gains: the counterfactual advantage produces the highest critic improvement rate of any model evaluated, indicating that trained critics learn to correct generator errors rather than to imitate them.
A capacity-headroom hypothesis is proposed, which states that PPO performance at the SLM scale depends on both a fluent supervised model and a discriminative reward signal, rather than on the number of model parameters.
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.
A three-paradigm taxonomy (feature-based, auxiliary-based, and policy-based) based on the functional role of LLMs within the RL pipeline is proposed, which provides superior scalability and stability, though often at the expense of representational depth.
A Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework that utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code.
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