kalomaze @kalomaze
kalomaze @kalomaze · 1m
prime-rl has a cute toy task for text reversal on Qwen 0.5b, which is used to debug simpler things (i.e. custom wandb logging, implementation correctness).
it uses 2 GPUs total and takes less than 5 minutes to finish.
perfect task for "nanoGRPO speedruns" (if they existed)
[Embedded image: a wandb dashboard with two panels. Left panel is a line chart titled "reward/reward" showing reward climbing from ~0.2 to plateau around 0.75-0.8 over ~25 training steps. Right panel is a table titled "runs.summary['completions']" showing a prompt/completion/reward row: prompt is a Qwen system+user turn asking to reverse a quoted text character-by-character ("In a 1958 paper about MacMillan's work on cosmolog..."), completion shows a reversed-text answer in <answer> tags, reward 0.8932.]
Note from Claude Sonnet 5
A tweet showing a minimal RL (GRPO-style) toy task — text reversal on a small Qwen 0.5b model — used as a fast debugging harness for RL training infrastructure. Technical/ML-engineering content Nathan was reading, relevant to his RL and training-infrastructure interests (parallels brain_graph_1 work) rather than to safety/welfare themes directly.
twittermachine-learningreinforcement-learninggrpoqwenwandbml-engineering