Antidistillation Fingerprinting

Model distillation enables efficient emulation of frontier large language models (LLMs), creating a need for robust mechanisms to detect when a third-party student model has trained on a teacher model's outputs. However, existing fingerprinting techniques that could be used to detect such distillation rely on heuristic perturbations that impose a steep trade-off between generation quality and fingerprinting strength, often requiring significant degradation of utility to ensure the fingerprint is effectively internalized by the student. We introduce antidistillation fingerprinting (ADFP), a principled approach that aligns the fingerprinting objective with the student's learning dynamics. Building upon the gradient-based framework of antidistillation sampling, ADFP utilizes a proxy model to identify and sample tokens that directly maximize the expected detectability of the fingerprint in the student after fine-tuning, rather than relying on the incidental absorption of the un-targeted biases of a more naive watermark. Experiments on GSM8K and OASST1 benchmarks demonstrate that ADFP achieves a significant Pareto improvement over state-of-the-art baselines, yielding stronger detection confidence with minimal impact on utility, even when the student model's architecture is unknown.

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

Reinforcement learning (RL) has emerged as a powerful paradigm for enhancing reasoning capabilities in large language models, but faces a fundamental asymmetry in computation and memory requirements: inference is embarrassingly parallel with a minimal memory footprint, while policy updates require extensive synchronization and are memory-intensive. To address this asymmetry, we introduce PODS (Policy Optimization with Down-Sampling), a framework that strategically decouples these phases by generating numerous rollouts in parallel but updating only on an informative subset. Within this framework, we develop max-variance down-sampling, a theoretically motivated method that selects rollouts with maximally diverse reward signals. We prove that this approach has an efficient algorithmic solution, and empirically demonstrate that GRPO with PODS using max-variance down-sampling achieves superior performance over standard GRPO on the GSM8K benchmark.