Learn Hard Problems During RL with Reference Guided Fine-tuning
Reinforcement learning (RL) for mathematical reasoning can suffer from reward sparsity: for challenging problems, LLM fails to sample any correct trajectories, preventing RL from receiving meaningful positive feedback. At the same time, there often exist human-written reference solutions along with the problem (e.g., problems from AoPS), but directly fine-tuning on these solutions offers no benefit because models often cannot imitate human proofs that lie outside their own reasoning distribution. We introduce Reference-Guided Fine-Tuning (ReGFT), a simple and effective method that utilizes human-written reference solutions to synthesize positive trajectories on hard problems and train on ...
| Authors | Yangzhen Wu, Shanda Li, Zixin Wen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 March 2026 |
| Last updated at source | 5 March 2026 |
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