Extending RLVR to Open-ended Tasks Without Verifiers

Establish effective strategies to extend Reinforcement Learning from Verifiable Rewards (RLVR) to open-ended natural language tasks, such as creative writing and brainstorming, that lack objective ground-truth verifiers, enabling reliable learning despite the absence of verifiable rewards.

Background

Reinforcement Learning from Verifiable Rewards (RLVR) has shown strong success in domains like mathematics and coding where objective verifiers exist, enabling clear, automatically checkable reward signals.

For open-ended tasks (e.g., creative writing, brainstorming), there is no objective ground truth or easy verification mechanism, making it difficult to create reliable reward signals. The paper highlights this as an unresolved challenge and motivates rubric-based judges as a step toward addressing it, while noting the broader problem of achieving RLVR-like reliability in non-verifiable domains remains open.

References

Extending this success to open-ended tasks (e.g., creative writing, brainstorming) remains an open challenge due to the lack of ground-truth verifiers~\citep{zhang2025auditable, simonds2025self}.

— Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks  (2602.05125 - Shen et al., 4 Feb 2026) in Related Works, Reinforcement Fine-Tuning (RFT) for Open-Ended Tasks

Extending the protocol to partially verifiable domains is open.

— Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents  (2609.01245 - Pu et al., 1 Sep 2026) in Technical Appendix, Section 'Scope of the Claims'

Our analysis and experiments are restricted to RLVR on tasks such as mathematical reasoning and code generation; we do not validate the proposed geometry on RLHF, multi-turn agentic decision-making, or open-ended generation, where whether the same properties hold remains open.

— Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors  (2609.34344 - Cai et al., 28 Sep 2026) in Section 5, Limitations and Future Work

Experiments are limited to mathematical reasoning with Qwen3 models and rule-based verifiers, leaving generalization to other domains, model families, and noisy or subjective reward signals untested.

— Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target  (2610.12830 - Cheng et al., 9 Oct 2026) in Section Limitations