Evaluate APSD on Additional Language-Generation Tasks

Evaluate the performance of Asymmetric Policy Optimization via Self-Distillation (APSD) on language-generation tasks beyond scientific reasoning, tool use, and code generation.

Background

The paper evaluates APSD in reinforcement learning with verifiable rewards and reinforcement learning with rich feedback, using tasks involving scientific reasoning, tool use, and code generation. It does not establish whether the method generalizes to other language-generation settings, leaving evaluation on additional language-generation tasks unresolved.

References

Our experiments cover diverse tasks, including scientific reasoning, tool use, and code generation, while evaluation on additional language generation tasks is left for future work.

— Correctness-Dependent Asymmetric Target Distributions for Self-Distillation Policy Optimization  (2610.25973 - Yoon et al., 19 Oct 2026) in Section: Limitations