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Open problems in Teacher policy distillation

Teacher recoverability on incorrect student rollouts1 paper raises it · 1 addresses it
Teachers often follow student errors instead of correcting them, reducing the quality of on-policy distillation supervision.
- Recovering Reliable Supervision via Critique-Guided On-Policy Distillationraises · addresses
Gradient conflicts in multi-teacher on-policy distillation1 paper raises it · 2 address it
Divergent teacher supervision can cause parameter updates to interfere, degrading student capabilities during joint optimization.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
- Latent-MOPD: Latent Multi-Teacher On-Policy Distillationaddresses
Tokenization mismatch in cross-tokenizer distillation1 paper raises it · 2 address it
Different tokenizers between teacher and student complicate alignment, making supervision unreliable or incomplete.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
- ActionOPD: Distilling Decisions, Not Tokensaddresses
Policy shift opacity in teacher distillation1 paper raises it · 1 addresses it
Endpoint policy shifts do not reveal whether local action preferences evolved consistently during teacher training, limiting selective transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Collapse in iterative agent self-distillation1 paper raises it · 1 addresses it
Successive distillation cycles can degrade deployment performance and privileged-information-conditioned behavior, hindering recursive self-improvement.
- ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillationraises · addresses

Named by an AI from these papers' abstracts.

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teacher policy distillation · policy distillation opd · teacher supervision318 papers40 this week
ai tutors · ai tutor · tutoring55 papers7 this week

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Active contributors · 90 days

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Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.LGSep 23
Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following
Yanzhao Zheng, Yuanqiang Yu, Tianze Xu, Chao Ma, Zhentao Zhang +4
Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tuned models still routinely violate some of them. Existing approaches either augment supervision with sequence- or token-level RL rewards from external verifiers or learned graders, or use on-policy distillation (OPD) against a single full-context teacher whose probability mass becomes diluted as more constraints become simultaneously active. We propose CC-OPD (Counterfactual Constraint-Conditioned On-Policy Distillation), which inverts the standard supervision-generation direction in distillation. Rather than enriching the teacher with information beyond what the student sees, CC-OPD ablates each constraint from the teacher's conditioning in turn, and constructs the per-constraint signal from the resulting per-token probability differentials. The resulting per-token leave-one-out log-likelihood shifts are summed, clipped, and added to the vanilla OPD reward as a token-level shaping term. All shaping terms are obtained from the frozen teacher, without an external verifier during distillation, and the reward equals vanilla OPD…
PDF arXiv
arXiv cs.AISep 23
Learn How to Act from Your Own Interactions: On-Policy Self-Distillation for GUI Agents
Yan Zhang, Daiqing Wu, Huawen Shen, Liang Li, Gang Cao +5
Graphical User Interface (GUI) agents enable the fulfillment of complex user instructions through multi-turn interactions with software environments, requiring step-wise reasoning and long-horizon memory to guide actions and retain task-relevant information, respectively. Recent on-policy self-distillation (OPSD) methods have achieved strong performance on GUI grounding, a foundational subtask for GUI agents, owing to dense token-level supervision from privilege-conditioned self-teachers. However, extending existing OPSD methods to multi-turn GUI agents is hindered by self-teachers' limited privilege-following ability and insufficient privileged guidance. In this paper, we introduce GUI-SD-v2, the next version of GUI-SD, which extends OPSD from GUI grounding to multi-turn GUI interaction and addresses key limitations through a two-stage training framework. Specifically, GUI-SD-v2 first strengthens privilege following by jointly optimizing rollouts with and without privileged guidance from the same GUI states. Furthermore, it selectively distills step-specific reasoning and memory guidance through a privilege-conditioned self-teacher, supporting action decisions and the retention…
PDF arXiv
arXiv cs.AISep 23
Conflicting Supervision Moves Commitment, Not Capability: A 12.29σ arrangement effect that is exactly zero under a convention-agnostic score
Wenhui Chen
"Train a model on the same problems written under two incompatible conventions, both correct, and ask what the ordering of that data writes into the parameters. The learning-rate schedule is not a background condition for that question. It is the averaging operator, and it decides the answer. We prove a bound in which the arrangement and the schedule enter the ordering effect as separate multiplied factors: the arrangement only as a block period, the schedule only as how much weight the endpoint can place on any one moment of the run. A decaying schedule cannot put a large step size and an uncontracted remainder at the same moment; a constant one does exactly that at the last step. That decay moderates ordering effects has been reported in pretraining; the mechanism, the separation, and a controlled measurement of both halves are ours. Ten orderings of one corpus, one budget, everything but the path held fixed, run twice under families differing in lr_scheduler_type and nothing else: at a constant rate the interior spans 0.2221 in allocation, 11.63 contrast floors, monotone in how blocked the arrangement is. Under the single cosine every published arm uses, the same ten arms…
PDF arXiv
arXiv cs.CLSep 22
EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues
Julian Bernado, Ana Trindade Ribeiro, Xander Beberman, Susanna Loeb
Large language models have enabled rapid deployment of language classification and analysis, allowing users to describe complex constructs in natural language, such as pedagogical moves or expressions of learning and engagement, and label them in conversational datasets such as classroom or tutoring transcripts. However, because LLM reasoning is opaque, we have no verifiable, mechanistic insight into why a model chose a label for an utterance, and output differences over time or across models challenge replication. We introduce the EduBehaviors framework to generate interpretable, reliable, and scalable construct annotations in educational dialogue data that retain human agency while leveraging the strengths of generative AI, encoders, and machine learning models. We evaluate the framework on the TalkMoves dataset, predicting the Teacher TalkMoves labels. Our best configuration results in a macro-F1 of 0.764 and 0.756 Cohen's kappa, demonstrating improvements over direct prompting approaches and showing competitiveness with fine-tuned classifiers while gaining the benefit of greater transparency and comparability. To facilitate adoption and operationalization of the EduBehaviors…
PDF arXiv
arXiv cs.LGSep 22
Train Where the Quantized Model Goes: On-Policy Distillation for Low-Bit Reasoning
Yuanteng Chen, Zhilei Liu, Peisong Wang, Yuantian Shao, Chuangyi Li +5
Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathematical and code reasoning substantially impaired. Long generations often degenerate into repetitive loops, exhausting the decoding budget without completing a solution. We trace this gap to quantization-amplified exposure bias: QAD trains on fixed corpus prefixes, while quantization-induced deviations compound along the model's own autoregressive trajectories. To address this mismatch, we introduce an on-policy distillation (OPD) stage that places teacher supervision where the quantized model actually goes. Starting from a QAD checkpoint, the student generates through the quantized forward path used at deployment and receives feedback from a frozen full-precision teacher on its own prefixes, combining dense token-level guidance with task-verifier rewards. Across four models at 2.79 and 1.88 effective bits, OPD raises average BF16 performance retention from 35% to 70% on MATH-500 and from 66% to 91% on HumanEval while preserving short-form performance, with reasoning gains substantially exceeding those of continued teacher-forced QAD…
PDF arXiv
arXiv cs.CVSep 22
BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception
Zihan Chen, Hengguang Zhou, Yuan Kang, Yiming Zhang, Wenhui Fang +3
Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may only appear in local regions. On-policy self-distillation (OPD) enables transferring privileged visual knowledge from informative views to full-image policies, but querying the teacher for every rollout introduces substantial supervision costs. In this work, we propose BAS-OPD, a budget-aware selective OPD framework that allocates teacher supervision under limited query budgets. Instead of querying all rollouts, BAS-OPD selects informative samples while maintaining full-batch student generation. We explore random, uncertainty-based, and learned utility-based selection strategies, where the learned selector estimates query value from detached rollout statistics and online utility signals derived from student--teacher agreement and teacher confidence without additional student forward passes. BAS-OPD only changes training-time supervision allocation and preserves single-pass full-image inference. Experiments on fine-grained multimodal perception benchmarks demonstrate that BAS-OPD achieves strong performance while substantially…
PDF arXiv

Papers

Topics in this field

teacher policy distillation · policy distillation opd · teacher supervision318 papers40 this week
ai tutors · ai tutor · tutoring55 papers7 this week

Recent papers

All papers in Research

arXiv cs.LGSep 23
Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following
Yanzhao Zheng, Yuanqiang Yu, Tianze Xu, Chao Ma, Zhentao Zhang +4
Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tuned models still routinely violate some of them. Existing approaches either augment supervision with sequence- or token-level RL rewards from external verifiers or learned graders, or use on-policy distillation (OPD) against a single full-context teacher whose probability mass becomes diluted as more constraints become simultaneously active. We propose CC-OPD (Counterfactual Constraint-Conditioned On-Policy Distillation), which inverts the standard supervision-generation direction in distillation. Rather than enriching the teacher with information beyond what the student sees, CC-OPD ablates each constraint from the teacher's conditioning in turn, and constructs the per-constraint signal from the resulting per-token probability differentials. The resulting per-token leave-one-out log-likelihood shifts are summed, clipped, and added to the vanilla OPD reward as a token-level shaping term. All shaping terms are obtained from the frozen teacher, without an external verifier during distillation, and the reward equals vanilla OPD…
PDF arXiv
arXiv cs.AISep 23
Learn How to Act from Your Own Interactions: On-Policy Self-Distillation for GUI Agents
Yan Zhang, Daiqing Wu, Huawen Shen, Liang Li, Gang Cao +5
Graphical User Interface (GUI) agents enable the fulfillment of complex user instructions through multi-turn interactions with software environments, requiring step-wise reasoning and long-horizon memory to guide actions and retain task-relevant information, respectively. Recent on-policy self-distillation (OPSD) methods have achieved strong performance on GUI grounding, a foundational subtask for GUI agents, owing to dense token-level supervision from privilege-conditioned self-teachers. However, extending existing OPSD methods to multi-turn GUI agents is hindered by self-teachers' limited privilege-following ability and insufficient privileged guidance. In this paper, we introduce GUI-SD-v2, the next version of GUI-SD, which extends OPSD from GUI grounding to multi-turn GUI interaction and addresses key limitations through a two-stage training framework. Specifically, GUI-SD-v2 first strengthens privilege following by jointly optimizing rollouts with and without privileged guidance from the same GUI states. Furthermore, it selectively distills step-specific reasoning and memory guidance through a privilege-conditioned self-teacher, supporting action decisions and the retention…
PDF arXiv
arXiv cs.AISep 23
Conflicting Supervision Moves Commitment, Not Capability: A 12.29σ arrangement effect that is exactly zero under a convention-agnostic score
Wenhui Chen
"Train a model on the same problems written under two incompatible conventions, both correct, and ask what the ordering of that data writes into the parameters. The learning-rate schedule is not a background condition for that question. It is the averaging operator, and it decides the answer. We prove a bound in which the arrangement and the schedule enter the ordering effect as separate multiplied factors: the arrangement only as a block period, the schedule only as how much weight the endpoint can place on any one moment of the run. A decaying schedule cannot put a large step size and an uncontracted remainder at the same moment; a constant one does exactly that at the last step. That decay moderates ordering effects has been reported in pretraining; the mechanism, the separation, and a controlled measurement of both halves are ours. Ten orderings of one corpus, one budget, everything but the path held fixed, run twice under families differing in lr_scheduler_type and nothing else: at a constant rate the interior spans 0.2221 in allocation, 11.63 contrast floors, monotone in how blocked the arrangement is. Under the single cosine every published arm uses, the same ten arms…
PDF arXiv
arXiv cs.CLSep 22
EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues
Julian Bernado, Ana Trindade Ribeiro, Xander Beberman, Susanna Loeb
Large language models have enabled rapid deployment of language classification and analysis, allowing users to describe complex constructs in natural language, such as pedagogical moves or expressions of learning and engagement, and label them in conversational datasets such as classroom or tutoring transcripts. However, because LLM reasoning is opaque, we have no verifiable, mechanistic insight into why a model chose a label for an utterance, and output differences over time or across models challenge replication. We introduce the EduBehaviors framework to generate interpretable, reliable, and scalable construct annotations in educational dialogue data that retain human agency while leveraging the strengths of generative AI, encoders, and machine learning models. We evaluate the framework on the TalkMoves dataset, predicting the Teacher TalkMoves labels. Our best configuration results in a macro-F1 of 0.764 and 0.756 Cohen's kappa, demonstrating improvements over direct prompting approaches and showing competitiveness with fine-tuned classifiers while gaining the benefit of greater transparency and comparability. To facilitate adoption and operationalization of the EduBehaviors…
PDF arXiv
arXiv cs.LGSep 22
Train Where the Quantized Model Goes: On-Policy Distillation for Low-Bit Reasoning
Yuanteng Chen, Zhilei Liu, Peisong Wang, Yuantian Shao, Chuangyi Li +5
Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathematical and code reasoning substantially impaired. Long generations often degenerate into repetitive loops, exhausting the decoding budget without completing a solution. We trace this gap to quantization-amplified exposure bias: QAD trains on fixed corpus prefixes, while quantization-induced deviations compound along the model's own autoregressive trajectories. To address this mismatch, we introduce an on-policy distillation (OPD) stage that places teacher supervision where the quantized model actually goes. Starting from a QAD checkpoint, the student generates through the quantized forward path used at deployment and receives feedback from a frozen full-precision teacher on its own prefixes, combining dense token-level guidance with task-verifier rewards. Across four models at 2.79 and 1.88 effective bits, OPD raises average BF16 performance retention from 35% to 70% on MATH-500 and from 66% to 91% on HumanEval while preserving short-form performance, with reasoning gains substantially exceeding those of continued teacher-forced QAD…
PDF arXiv
arXiv cs.CVSep 22
BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception
Zihan Chen, Hengguang Zhou, Yuan Kang, Yiming Zhang, Wenhui Fang +3
Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may only appear in local regions. On-policy self-distillation (OPD) enables transferring privileged visual knowledge from informative views to full-image policies, but querying the teacher for every rollout introduces substantial supervision costs. In this work, we propose BAS-OPD, a budget-aware selective OPD framework that allocates teacher supervision under limited query budgets. Instead of querying all rollouts, BAS-OPD selects informative samples while maintaining full-batch student generation. We explore random, uncertainty-based, and learned utility-based selection strategies, where the learned selector estimates query value from detached rollout statistics and online utility signals derived from student--teacher agreement and teacher confidence without additional student forward passes. BAS-OPD only changes training-time supervision allocation and preserves single-pass full-image inference. Experiments on fine-grained multimodal perception benchmarks demonstrate that BAS-OPD achieves strong performance while substantially…
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 6m ago

Teacher policy distillation

Found by the topic model: teacher policy distillation, ai tutors.

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Open problems in Teacher policy distillation

Teacher recoverability on incorrect student rollouts1 paper raises it · 1 addresses it
Teachers often follow student errors instead of correcting them, reducing the quality of on-policy distillation supervision.
- Recovering Reliable Supervision via Critique-Guided On-Policy Distillationraises · addresses
Gradient conflicts in multi-teacher on-policy distillation1 paper raises it · 2 address it
Divergent teacher supervision can cause parameter updates to interfere, degrading student capabilities during joint optimization.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
- Latent-MOPD: Latent Multi-Teacher On-Policy Distillationaddresses
Tokenization mismatch in cross-tokenizer distillation1 paper raises it · 2 address it
Different tokenizers between teacher and student complicate alignment, making supervision unreliable or incomplete.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
- ActionOPD: Distilling Decisions, Not Tokensaddresses
Policy shift opacity in teacher distillation1 paper raises it · 1 addresses it
Endpoint policy shifts do not reveal whether local action preferences evolved consistently during teacher training, limiting selective transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Collapse in iterative agent self-distillation1 paper raises it · 1 addresses it
Successive distillation cycles can degrade deployment performance and privileged-information-conditioned behavior, hindering recursive self-improvement.
- ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillationraises · addresses

Named by an AI from these papers' abstracts.