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.

References

While effective, these methods introduce new experimental confounds—it becomes unclear whether performance gains stem from pretraining, RL fine-tuning, stochastic training dynamics, or architectural strength.

— Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers  (2512.17351 - Allen-Zhu, 19 Dec 2025) in Section 1 (Introduction), Challenge 3: Grokking, Data Quality, and Curriculum Learning

Neither benchmark was measured after Search Agent, so we cannot say which of the two later stages moved them.

— Rufus-Air: An Open LLM Post-Training Recipe  (2609.29421 - Chang et al., 24 Sep 2026) in Section 3.6, subsection “Coding Agent,” paragraph “Result anchor” (Caveat)

Several settings changed together, so these comparisons do not isolate the cause of the improvement.

— Faynt: Scaling and Optimizing Policies for Competitive Melee  (2610.02144 - Janati et al., 1 Oct 2026) in Section 3.4, “Reinforcement Learning”

The study also lacks compute-matched continuation controls and independent training replications, so the contribution of each intervention cannot be isolated.

— Faynt: Scaling and Optimizing Policies for Competitive Melee  (2610.02144 - Janati et al., 1 Oct 2026) in Appendix E, Section “Scope of the Comparisons”

Whether stronger initial multi-token capability enables reliable coding gains remains an open question.

— A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs  (2610.18200 - Kuang et al., 13 Oct 2026) in Limitations section

The checkpoint and preference-data comparisons are observational, so they show that frequency rises along training pipelines but do not establish which stage or data causes the rise.

— Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness  (2610.22249 - Peng et al., 15 Oct 2026) in Limitations, paragraph “Observational training evidence”; Discussion and Conclusion