Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO [P]

Reddit r/MachineLearning / 4/13/2026

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Key Points

A Reddit user retrained a small Qwen2.5-0.5B-Instruct bf16 model for Reddit post summarization using GRPO (RLVR), targeting a summary length they intended to be 64 tokens but mistakenly set as 64 characters.
They observed W&B metrics where average response length collapsed and saturated around 10–15 tokens, attributing the issue to the character/token confusion.
The training used two rewards—(1) a length penalty based on deviation from MAX_LENGTH and (2) a quality reward using ROUGE-L against golden summaries to reduce reward gaming.
Including the ROUGE-L quality reward prevented degenerate behaviors seen in earlier runs with only the length penalty (e.g., generating filler “*20 tokens” content).
They report similar results across runs with/without the quality reward after one epoch, and plan next steps to debug GRPO’s reward “gaming,” test alternative metrics, and try judge-based evaluation (LLM-as-a-judge).

Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO [P]

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!

The wandb charts for avg response length was going down and saturated around 10-15 tokens on an avg. This was the result of me confusing between character counts and token counts, I meant to do 64 tokens but rather I accidentally went for 64 characters!

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:

length_penalty : basically, -abs(response_length - MAX_LENGTH)
quality_reward: a ROUGE-L, which is basically LCS of golden summarizations I had as part of the above dataset, to ensure we have some structure throughout the responses generated and minimize degradation.

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 -

without a quality reward in my previous runs, the system tried to game the rewards by outputting stuff like "-------*20" tokens thats it!
But not this time since I got the near same results for rewards of both the experiments when I included both vs just length penalty, and no degradation in the rollouts after 1 full epoch so I wonder why?

Anyways, next up:

Find out why GRPO didn't try other game the reward system?
Try out metrics other than ROUGE-L to get better summarizations maybe
Setup LLM-As-A-Judge to quantify the results.
Train some HF SmolLM series now!
What if I told in the prompt itself about the reward system and about the MAX_LENGTH with the task?
Different MAX_LENGTH?

https://preview.redd.it/mf7rux5lhyug1.png?width=800&format=png&auto=webp&s=bc54273f644ee2306b03834e037ab3e91f3b0582

https://preview.redd.it/1es4n61mhyug1.png?width=800&format=png&auto=webp&s=a8cc4249e646f03e8396cf79e640e27fcd1edfce

https://preview.redd.it/djsslwsmhyug1.png?width=800&format=png&auto=webp&s=91589c746ac7a2c43d724e4768e8cb610288dee4

submitted by /u/East-Muffin-6472
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