Consider two parameter count vs. training step curves, w/ an equivalent area under the curve, ie., training FLOPs. Solid line = dense pre-training, dashed line = sparse pre-training w/ gradual pruning. While they differ in final param count, they match in average param count. 3/N

The quality-speedup trade-off keeps improving with more training - showing no signs of saturation! We took 4 snapshots at different points of preference optimization (10% Round 1, 100% R1, 10% R2, 60% R2). As we train more, this trade-off improves toward the optimal top-right corner. 11/N

We show that PASTA Pareto-dominates all existing async decoding methods! We achieve geometric mean speedups ranging from 1.21× to 1.93× with corresponding quality changes of +2.2% to -7.1%, measured by length-controlled win rates against sequential decoding baseline. 10/N

Excited to share our work with friends from MIT/Google on Learned Asynchronous Decoding! LLM responses often contain chunks of tokens that are semantically independent. What if we can train LLMs to identify such chunks and decode them in parallel, thereby speeding up inference? 1/N

The quality-speedup trade-off keeps improving with more training - showing no signs of saturation! We took 4 snapshots at different points of preference optimization (10% Round 1, 100% R1, 10% R2, 60% R2). As we train more, this trade-off steadily improves toward the optimal top-right corner. 10/N

We show that PASTA Pareto-dominates all existing async decoding methods! We achieve geometric mean speedups ranging from 1.21× to 1.93× with corresponding quality changes of +2.2% to -7.1%, measured by length-controlled win rates against sequential decoding baseline. 9/N