Parameter Golf: What Really Works?
Quick Answer
This paper shows that The Parameter Golf challenge tested language model optimization under a strict 16 MB artifact budget, achieving a 13.6% reduction in bits-per-byte (BPB) from 1.2244 to 1.058 across 2,037 submissions.
Quick Take
The Parameter Golf challenge tested language model optimization under a strict 16 MB artifact budget, achieving a 13.6% reduction in bits-per-byte (BPB) from 1.2244 to 1.058 across 2,037 submissions. Despite individual techniques showing minimal improvements, a taxonomy of 84 methods was developed to isolate effective strategies.
Key Points
- Participants trained models within 10 minutes on 8xH100 SXM GPUs.
- Quality measured in bits-per-byte (BPB) for unseen text encoding.
- A total of 2,037 pull requests and 1,430 clean submissions were analyzed.
- Most optimization techniques showed diminishing returns in competitive settings.
- The leaderboard score demonstrated significant improvement through collaborative efforts.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2607. 01517v1 Announce Type: new Abstract: How far can a language model improve under a strict artifact budget? Parameter Golf posed this question as an open community challenge in which participants trained the best language model, with the complete artifact (training code + compressed weights) required to fit within 16 MB and be trained in under ten minutes on 8xH100 SXM GPUs. Quality was measured in bits-per-byte (BPB), the average number of bits required to encode each byte of unseen text.
We analyze 2,037 pull requests and 1,430 clean scored submissions from the contest, build a taxonomy of 84 optimization techniques, and measure each technique's contribution to BPB. The verified leaderboard score dropped from 1. 2244 to 1. 058 BPB across three phases -- a 13. 6% reduction, despite individual techniques rarely improving BPB by more than 1%. We show that most gains in techniques shrink across competitive submissions, isolating the few methods that improve performance across stacks.
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