ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where the objective is to maximize the collective utility of the entire set rather than individual candidates independently. However, existing reinforcement learning post-training paradigms, such as Group Relative Policy Optimization (GRPO), typically assign the same set-level scalar reward to every candidate in the set. This leads to noisy training signals where poor candidates free-ride on the high reward produced by a single strong peer, resulting in suboptimal exploration. To address this, we propose Shapley-Enhan...
| Authors | Rui Ai, Yu Pan, David Simchi-Levi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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