Identify sources of performance gains in RL-based post-training

Ascertain the primary contributors to performance improvements observed in large language models following reinforcement learning-based post-training methods such as GRPO and PPO by isolating and quantifying the effects of pretraining, RL fine-tuning procedures, stochastic training dynamics (including random seeds and data ordering), and intrinsic architectural strength, so that experimental confounds are removed and causal attributions become reliable.

Background

The paper highlights that RL-based post-training (e.g., GRPO and PPO) can deliver tailored data at optimal difficulty, improving model performance. However, these methods introduce experimental confounds that make it hard to determine whether gains come from the RL step itself, the preceding pretraining, stochastic training dynamics, or from the architecture. This uncertainty undermines rigorous architectural comparisons and motivates a controlled analysis to disentangle these factors.

By framing training and evaluation within synthetic tasks, the authors argue one can better attribute improvements to their true sources. Nonetheless, they explicitly note that it becomes unclear which component drives observed gains, marking this as an open question for careful study.