The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
arXiv:2606.29526v1 Announce Type: new Abstract: Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer from instability or collapse. One vital cause is training-inference mismatch: LLM adopts separate inference and training engines for generation efficiency and training precision, which in practice exhibits inconsistent probabilities for the same trajectories on training and inference sides, even with synchronized model parameters. This naturally induces a special type of off-policyness ever existing
The rapid deployment and scaling of LLMs are exposing fundamental limitations in current post-training methodologies, particularly as companies push for more robust and reliable AI systems.
This research addresses a core instability in Reinforcement Learning for LLMs, which, if resolved, could significantly improve the reliability, safety, and performance of advanced AI models.
A potential shift from optimizing training policies to focusing on monotonic inference policies for LLM reinforcement learning could lead to more stable and predictable AI behaviors.
- · AI research labs
- · Companies deploying LLMs
- · AI safety researchers
- · Cloud infrastructure providers
- · AI models with unstable RL tuning
- · Current fragile RL techniques
- · Early adopters facing model collapse
Improved stability and performance of large language models post-training.
Accelerated development and adoption of more reliable AI applications across various industries.
Enhanced trust in AI systems could lead to broader integration into critical societal functions.
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Read at arXiv cs.LG