Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
HP-RLVR gives single-rollout token credit for RLVR, training Qwen3 faster than GRPO with comparable accuracy.
The paper proposes Hindsight-Privileged RLVR, a single-rollout method that converts a completed trajectory's future and verifier feedback into bounded token-level credit without a reference solution or learned critic. In paired Qwen3-8B runs over 100 steps, it trained 1.21 times, 1.46 times, and 2.30 times faster than GRPO on mathematics, code generation, and ALFWorld while using one eighth as many rollouts. Overall accuracy was comparable: HP-RLVR led on mathematics at three sizes, while GRPO led on code and ALFWorld. In ALFWorld, 43.2% of 4B and 49.0% of 8B GRPO gradient-norm records were zero, versus none for HP-RLVR.
- HP-RLVR weights tokens from hindsight versus causal policy views.
- It needs one rollout per prompt, one eighth of GRPO's rollouts.
- Qwen3-8B trained 1.21x to 2.30x faster across math, code, and ALFWorld.
- Accuracy was comparable, with HP-RLVR ahead on mathematics.
- GRPO had many zero gradient norms in ALFWorld; HP-RLVR had none.
From the original article
1 min read at arxiv.orgReinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual reasoning tokens or actions remains difficult. Group-relative methods such as Group Relative Policy Optimization (GRPO) sample several attempts per prompt, but lose their reward-based learning signal when all attempts receive the same outcome.
By Wei Chen · arxiv.org
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