aiqbench analysis
This research identifies a critical flaw in Group Relative Policy Optimization (GRPO) where the advantage estimator inadvertently rewards guessing over reasoning, particularly in bounded-answer tasks. The authors introduce SIGNBALANCE, a composition-free magnitude method that restores zero-mean balance to prevent policies from converging toward spurious guess-like behaviors. By improving performance on math and search benchmarks without sacrificing open-answer capabilities, this work advances the reliability of reinforcement learning with verifiable rewards.
Spurious Advantage Hidden in GRPO
arXiv:2609.04063v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is widely studied for reinforcement learning with verifiable rewards, where its advantage estimator assigns each rollout a magnitude from within-group reward statistics. In the common case, this magnitude rewards rollouts that reach the correct answer through reasoning. Yet, an overlooked case shares the same surface: a rollout may land on it by guessing, and the formula still assigns a high magnitude, which we identify as the spurious advantage. This arises in three cases: bounded-answer tasks with a small candidate set; open-answer sets hosting bounded sub-cases; and search agents whose budget opens many paths to the same answer. In all three, this misleads the policy toward guess-like behaviors. We propose SIGNBALANCE, whose magnitude is composition-free: it keeps the verifier sign, uses a global scale, and restores zero-mean balance via a stop-gradient per-class rescaling. Across math and search agent benchmarks at different scales, SIGNBALANCE matches GRPO on open-answer math and improves on bounded-answer math and search agents. Code will be released.
Abstract:Group Relative Policy Optimization (GRPO) is widely studied for reinforcement learning with verifiable rewards, where its advantage estimator assigns each rollout a magnitude from within-group reward statistics. In the common case, this magnitude rewards rollouts that reach the correct answer through reasoning. Yet, an overlooked case shares the same surface: a rollout may land on it by guessing, and the formula still assigns a high magnitude, which we identify as the spurious advantage. This arises in three cases: bounded-answer tasks with a small candidate set; open-answer sets hosting bounded sub-cases; and search agents whose budget opens many paths to the same answer. In all three, this misleads the policy toward guess-like behaviors. We propose SIGNBALANCE, whose magnitude is composition-free: it keeps the verifier sign, uses a global scale, and restores zero-mean balance via a stop-gradient per-class rescaling. Across math and search agent benchmarks at different scales, SIGNBALANCE matches GRPO on open-answer math and improves on bounded-answer math and search agents. Code will be released.
Artificial Intelligence (cs.AI)
arXiv:2609.04063 [cs.AI]
(or arXiv:2609.04063v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.04063 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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