SAGE Improves GRPO Under Sparse Rewards
Researchers (Dong et al.) on Feb 3, 2026 propose SAGE, an on-policy RL framework that injects privileged compact hints during Group Relative Policy Optimization (GRPO) training to increase within-group outcome diversity and prevent advantage collapse under sparse terminal verifier rewards. They evaluate SAGE across six benchmarks with three LLMs, reporting average improvements of +2.0 (Llama-3.2-3B), +1.2 (Qwen2.5-7B), and +1.3 (Qwen3-4B); code released.
Key Points
- 1Introduces SAGE that injects compact privileged hints during on-policy GRPO training to reshape rollouts
- 2Addresses advantage collapse by increasing within-group outcome diversity under identical terminal verifier rewards
- 3Enables deploying no-hint policies at test time after training with hints, improving alignment performance
Scoring Rationale
Strong methodological contribution with empirical LLM gains and released code; limited by being a single-source arXiv preprint.
Sources
Public references used for this report.
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