Research · Updated 6 Oct, 09:30 am IST
SYNLAT aligns compression with syntax to shrink chain-of-thought outputs
Why it matters for readers: It shows we can shorten step-by-step AI explanations smartly by following syntax instead of chopping text randomly.
- SYNLAT segments reasoning into non-overlapping Syntax-Aligned Units (SAUs) so compression boundaries respect syntactic structure.1
- A teacher constructs progressive KEEP/LATENT targets while a single compression-conditioned student generates mixed reasoning at inference using only the question and a requested compression level.1
- Evaluations use Qwen3 Student scales across Standard-CoT and Long-CoT groups and three compression levels.1
- SYNLAT matches or exceeds the strongest baseline in 11 of 12 task-group aggregates under the reported selection protocol.1
- Reported overall gains reach about 3.6/2.6 points at MEDIUM and 7.0/5.5 points at HIGH compression for Qwen3-8B/14B respectively, with larger advantages under stronger compression.1
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