Post-training shouldn't cost you diversity. With Gacha Decoding 🎲, it doesn't.
The idea: treat diversity as an instruction-following task and give the model an RNG tool. Unlike prior methods, it gets better as models get stronger.
Reroll your LM 🎲 arxiv.org/abs/2610.01382
The idea: treat diversity as an instruction-following task and give the model an RNG tool. Unlike prior methods, it gets better as models get stronger.
Reroll your LM 🎲 arxiv.org/abs/2610.01382
arxiv.org Gacha Decoding: Eliciting Diverse Generations Through Instruction Following We introduce Gacha Decoding, an inference-time method for eliciting diverse language model generations that scales with model capability. Across open-ended domains (in-the-wild chat, creative... Introducing Gacha Decoding 🎲: we show how to diversify LM responses, beating prior work by >11x in sample efficiency.
Reroll your LM for varied ideas, data, RL envs, {your use here}!
We get diversity from instruction following + external RNG, not token entropy. Here's how 🧵View quoted post on X
