7B, 4B and 8B parameters on mathematics, code generation and ALFWorld, an interactive household-task environment.
Hindsight assigns token credit from one verified language-model rollout
HP-RLVR uses a trajectory’s realized future and verifier feedback to weight token updates without group sampling, reference answers or a learned critic.
Chinese Tech
Wei Chen · Yubing Wu · Zhou Xu · Haoyue Zhang · Delu Zeng · Jiahao Wang · +6 more
South China University of Technology · Tsinghua University · The Hong Kong University of Science and Technology · Huawei 2012 Laboratories · Shenzhen Technology University
Research Digest··2 min read
Chen and colleagues propose Hindsight-Privileged RLVR, a reinforcement-learning method that extracts token-level credit from a single completed attempt.
Why this paper
From Huawei 2012 Laboratories and 5 others
In one line
HP-RLVR assigns token credit from a single rollout per prompt, training 1.21x to 2.30x faster than GRPO with comparable accuracy.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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