How to fine-tune an LLM for open-ended problems? [P]

I want to develop an LLM that can solve open-ended math problems (such as proof-only problems). This means that RLVR where we use the final answer alone as reward signal is not enough. Since SFT is useless here and GRPO/PPO methods will not have an appropriate reward function, what kind of fine-tuning can I do? For data, I will use the MathNet dataset. submitted by /u/TechNerd10191 [link] [comments]