Information-Time Proximal Policy Optimization
Quick summary
arXiv:2609.24380v1 Announce Type: cross Abstract: RLVR has substantially improved the reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by token-by-token generation, despite the highly non-uniform information flow along autoregressive trajectories. In this paper, we propose InfoPPO, which reparameterizes temporal progression using information density rather than raw token count. This reparameterization induces a common state-dependent structure for both temporal credit propagation and policy updates. InfoPPO res
Key takeaways
- arXiv:2609.24380v1 Announce Type: cross Abstract: RLVR has substantially improved the reasoning capabilities of LLMs.
- However, existing methods typically parameterize temporal progression in the Markov Decision Process by token-by-token generation, despite the highly non-uniform information flow along autoregressive trajectories.
- In this paper, we propose InfoPPO, which reparameterizes temporal progression using information density rather than raw token count.
Why it matters
“Information-Time Proximal Policy Optimization” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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