Summary of Provable Interactive Learning with Hindsight Instruction Feedback, by Dipendra Misra et al.
Provable Interactive Learning with Hindsight Instruction Feedback
by Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new paper explores interactive learning, where an AI system generates a response based on context and instruction. Unlike traditional approaches that rely on rewards or expert supervision for training, this study focuses on “hindsight labeling” – the teacher provides an instruction tailored to the agent’s generated response. This approach is often easier than providing optimal response supervision. The paper initiates theoretical analysis of interactive learning with hindsight labeling, establishing a lower bound showing regret scaling with the size of the response space. It also introduces LORIL, an algorithm for decomposable instruction-response distributions, which achieves regret scaling as sqrt(T), where T is the number of rounds. Experimental results in two domains show that LORIL outperforms baselines even when the low-rank assumption is violated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI learns to respond to instructions by generating actions or trajectories based on context and guidance from a teacher. This paper explores “hindsight labeling,” where teachers provide instructions suitable for AI-generated responses. The approach is easier than providing expert supervision, which may require specialized knowledge. Researchers analyzed interactive learning with hindsight labeling, showing that regret must scale with the response space size. They also developed LORIL, an algorithm for decomposable instruction-response distributions, and tested it in two domains. |
