# Iterative Teacher-Aware Learning

arXiv: https://arxiv.org/abs/2110.00137 · HTML: https://mlsanity.com/paper/2110.00137/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 79 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Reasoning About Pragmatics with Neural Listeners and Speakers](https://mlsanity.com/paper/1604.00562/) (2016)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [Learning to Teach with Dynamic Loss Functions](https://mlsanity.com/paper/1810.12081/) (2018)
- [Pragmatic-Pedagogic Value Alignment](https://mlsanity.com/paper/1707.06354/) (2017)
- [A mathematical theory of cooperative communication](https://mlsanity.com/paper/1910.02822/) (2019)
- [Towards Black-box Iterative Machine Teaching](https://mlsanity.com/paper/1710.07742/) (2017)
- [Teaching Inverse Reinforcement Learners via Features and Demonstrations](https://mlsanity.com/paper/1810.08926/) (2018)
- [Machine Teaching for Bayesian Learners in the Exponential Family](https://mlsanity.com/paper/1306.4947/) (2013)
- [Emergence of Pragmatics from Referential Game between Theory of Mind Agents](https://mlsanity.com/paper/2001.07752/) (2020)
- [Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners](https://mlsanity.com/paper/1802.05190/) (2018)
- [Incorporating Pragmatic Reasoning Communication into Emergent Language](https://mlsanity.com/paper/2006.04109/) (2020)
- [Becoming the Expert - Interactive Multi-Class Machine Teaching](https://mlsanity.com/paper/1504.07575/) (2015)
- [Planning, Inference and Pragmatics in Sequential Language Games](https://mlsanity.com/paper/1805.11774/) (2018)
- [Machine Teaching of Active Sequential Learners](https://mlsanity.com/paper/1809.02869/) (2018)
- [An Incremental Iterated Response Model of Pragmatics](https://mlsanity.com/paper/1810.00367/) (2018)
- [An Optimal Control Approach to Sequential Machine Teaching](https://mlsanity.com/paper/1810.06175/) (2018)
- [Optimal Cooperative Inference](https://mlsanity.com/paper/1705.08971/) (2017)
- [Generalizing the theory of cooperative inference](https://mlsanity.com/paper/1810.02423/) (2018)
- [Sequential Cooperative Bayesian Inference](https://mlsanity.com/paper/2002.05706/) (2020)
- [Interpretable and Pedagogical Examples](https://mlsanity.com/paper/1711.00694/) (2017)
- [Online Inverse Reinforcement Learning via Bellman Gradient Iteration](https://mlsanity.com/paper/1707.09393/) (2017)

## Built on by (most cited first, 7 of 7)

- [CodePDE: An Inference Framework for LLM-driven PDE Solver Generation](https://mlsanity.com/paper/2505.08783/) (2025)
- [Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction](https://mlsanity.com/paper/2506.07976/) (2025)
- [CAT: Content-Adaptive Image Tokenization](https://mlsanity.com/paper/2501.03120/) (2025)
- [A Review on Machine Theory of Mind](https://mlsanity.com/paper/2303.11594/) (2023)
- [Iterative Teaching by Data Hallucination](https://mlsanity.com/paper/2210.17467/) (2022)
- [Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization](https://mlsanity.com/paper/2503.07660/) (2025)
- [From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort](https://mlsanity.com/paper/2610.20396/) (2026)

## Abstract (arXiv)

In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher's instruction mechanism, can infer the teacher's intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn't been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers.