•2 min read•from Machine Learning
Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO [P]
Our take
In a recent experiment, I trained a Qwen2.5-0.5B-Instruct model on the smoltldr Reddit post summarization dataset, targeting concise outputs of 64 tokens. However, I mistakenly set the limit to 64 characters, resulting in average responses of only 10-15 tokens. By incorporating both a length penalty and a quality reward based on ROUGE-L, the model avoided previous pitfalls of producing generic outputs. Moving forward, I plan to explore alternative metrics, adjust the MAX_LENGTH, and investigate the implications of clearly communicating the reward system in prompts.
![Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO [P]](https://preview.redd.it/mf7rux5lhyug1.png?width=140&height=90&auto=webp&s=ca85312bbdb852edbfcd38808c9111e272c70d16)
| So, a few days back I shared a post where I trained a tiny Qwen2.5-0.5B-Instruct model on smoltldr (reddit post summarization dataset of 2k rows), to output summaries of about 64 max length using RLVR with GRPO . However, there was a catch!
Hence the charts showed a sharp decline and convergence towards a response length of on and off 15 tokens. The rewards I used were 2:
Trained to one full epoch with a batch size of 2 max (before getting a OOM), the results were identical to the previous run, however, with one crucial difference -
Anyways, next up:
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