Gold-Conditioned Distillation is proposed, a method that turns expert demonstrations into model-native supervision and improves new-task accuracy over SFT and generally reduces degradation on general benchmarks.
Abstract
Supervised fine-tuning treats the expert demonstration or gold response as a one-hot target, suppressing valid alternative continuations that the model would otherwise assign probability to. We propose Gold-Conditioned Distillation (GCD), a method that turns expert demonstrations into model-native supervision. GCD uses one model as both teacher and student: the teacher conditions on a prompt containing the gold response in context, the student conditions on the original prompt as at inference, and the student is trained to minimize the forward KL divergence to the teacher's next-token distribution at each gold response token. This lets the model use its prior knowledge to teach itself the new task: correctness is anchored by the gold response, while the soft target preserves plausible alternatives. Across various training tasks on Qwen and Llama models, GCD improves new-task accuracy over SFT and generally reduces degradation on general benchmarks.
On-policy self-distillation (OPSD) trains a large language model using supervision from a teacher instantiated from the same model as the student. The teacher is conditioned on privileged information (PI), such as verified solutions. The student, which does not observe PI, learns from the teacher's next-token distribut...
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Activation-Conditioned Self-Distillation (ACSD), which extracts a steering vector by contrasting activations of self-generated trajectories that reach verified correct answers within a generation budget with those of all remaining trajectories, achieves the highest mean accuracy over four mathematical benchmarks among...
This work proposes soft-target fine-tuning (SoFT) to balance learning from teacher demonstrations with retaining the Base model's existing capabilities, with improvements in both in-distribution capability acquisition and out-of-distribution generalization.
Across the seven tasks and hyperparameter configurations the authors consider, CTCD is competitive with vanilla distillation and outperforms supervised fine-tuning alone on average; it transfers to task-specific fine-tuning as a viable distillation objective, but not as an improvement over the standard formulation.
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DCE+SRCL outperforms OPSD across multiple model scales and four competition-level mathematics benchmarks, and its comprehensive evaluations show that DCE+SRCL outperforms OPSD across multiple model scales and four competition-level mathematics benchmarks.
Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-model post-training. Recent trends suggest that multi-teacher on-policy distillation (MOPD) outperforms conventional off-policy methods. However, despite the higher inferen...
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