Interactive Learning in Language Models

• Interactive learning in language models is a dynamic paradigm that uses real-time, context-sensitive feedback to continuously update model parameters, thereby improving reasoning and data efficiency.
• It employs diverse frameworks such as teacher–student protocols, multi-agent interaction, and reinforcement learning to adapt strategies and reduce issues like hallucinations and language drift.
• Empirical benchmarks show significant accuracy improvements and robustness in tasks like language acquisition, task completion, and social intelligence through iterative, feedback-driven training.

Interactive learning in LMs encompasses a set of training protocols, architectures, and evaluation methodologies where LM updates are informed by ongoing interaction—either with other models, with simulated or real human users, or within partially observable environments. This dynamic, feedback-driven paradigm stands in contrast to static supervised learning, offering mechanisms for continual adaptation, reduction of hallucinations, improved data efficiency, and alignment with human developmental processes. Interactive learning frameworks have been advanced across supervised, reinforcement, and dual-checker settings, with empirical validation in language acquisition, task completion, reasoning, and social intelligence.

1. Core Definitions and Paradigms

Interactive learning in LMs involves updating model parameters or policies based on real-time or sequential feedback. Frameworks commonly deploy teacher–student architectures, multi-agent systems, or RL-based dynamics. Essential components include:

• Contextualized Feedback: Reward signals, rationales, or natural-language feedback directed at the learner instance.
• Iterated Loops: Cycles of trials, demonstrations, or multi-round dialogues driving updates and curriculum adjustment.
• Dynamic Interaction: Adaptive strategy selection based on task difficulty or model ability, e.g., via cooperative or competitive exchanges (Lin et al., 30 Sep 2025).

Distinct paradigms include:

• Interactive Distillation: Teacher supplies in-context rationales and predictions; student fine-tunes and feeds back difficult cases (e.g., DualChecker (Wang et al., 22 Aug 2024)).
• Question-driven Learning: Students actively query teachers, receiving targeted answers to refine understanding (INTERACT (Kendapadi et al., 16 Dec 2024)).
• Multi-agent Co-learning: Agents exchange ideas and calibrate rewards with peers, improving individual reasoning (ILR (Lin et al., 30 Sep 2025)).
• Imitation plus Reinforcement: Joint next-token prediction and policy-gradient objectives leveraging environment feedback (Listen, Interact and Talk (Zhang et al., 2017)).

2. Algorithmic Frameworks and Training Objectives

Interactive learning objectives vary by paradigm. Representative formulations include:

• DualChecker Distillation Losses:
  • ContextAligner applies semantic similarity to construct in-context prompts:

  $$\text{Similarity}(EMB_i,EMB_j)=\frac{EMB_i^\top EMB_j}{\|EMB_i\|\|EMB_j\|}$$ - Student cross-entropy:

  $$\mathcal{L}_\mathcal{T}^S = -\sum_{d\in\mathcal{D}} \log p(c^d|Token^d)$$ - Teacher/Student confidence checking and template refinement drive closed-loop feedback.

• Iterated Teacher–Student Protocols (SSIL (Lu et al., 2020)):

  • Teacher loss: $$L_{\text{SSIL}}^{(\text{teacher})} = L_{\text{interactive}} + \alpha L_{\text{supervised}}(\mathrm{human})$$
  • Student imitation: cross-entropy on teacher-generated data.
• RL-based Interactive Summarization (Stöpler et al., 9 May 2025):
  • Speaker policy $\pi_\theta$ updated to maximize communicative success:

  $$J(\theta) = \mathbb{E}_{c,q}\,\mathbb{E}_{s\sim\pi_\theta}\,\mathbb{E}_{a\sim\ell}\big[R(c,s,a)\big]$$

  with reward $R$ as ROUGE-L F1 minus length/surprisal-based penalty.

• Multi-agent Interaction and Reward Calibration (ILR (Lin et al., 30 Sep 2025)):
  • Interaction mode selection by question difficulty (IRT criteria).
  • Reward blending:

  $$\bar R_{i,k} = R_{i,k} + \sum_{l \neq i} \text{clip}\Big( \frac{R_{i,k} - R_{l,avg}}{R_{l,max} - R_{l,min}}, -\frac{1}{m-1}, +\frac{1}{m-1} \Big)$$ - GRPO gradient update per agent.

3. Benchmarks, Evaluation, and Data Efficiency

A spectrum of benchmarks and controlled environments are designed for interactive learning assessment.

• LLF-Bench (Cheng et al., 2023): Unified Gym API for sequential decision tasks with natural-language feedback (recommendation, poem writing, navigation, robot control), paraphrasing and environment randomization to avoid superficial fit.
• QAit (Yuan et al., 2019): Text-based, partially observable worlds requiring agents to seek information actively, with metrics like sufficient-information bonus and compositional generalization.
• Storytelling Evaluation (Martins et al., 19 Sep 2025): Teacher model rates student stories in terms of readability, coherence, and creativity; interactive learning shown to yield equivalent gains as 400x larger static text exposure.

Key findings:

• DualChecker yields up to 17 pp F1 improvement in teacher models and 10 pp in students for green innovation text classification (Wang et al., 22 Aug 2024).
• INTERACT demonstrates cold-start students match static-learning baselines within five dialogue turns, with up to +25% accuracy improvements (Kendapadi et al., 16 Dec 2024).
• High-level, cognitively inspired feedback in storytelling produces comparable narrative skill gains with just 1M words in the interactive loop versus 410M words of next-word prediction (Martins et al., 19 Sep 2025).

4. Mechanisms to Prevent Drift and Hallucination

Interactive frameworks directly address prevalent failure modes in LM learning:

• Language Drift: SSIL combines interaction and supervised replay in teacher updates, preserving human-like utterances and preventing the emergence of private symbolic codes (Lu et al., 2020).
• Hallucination Control: DualChecker uses confidence-based teacher re-prompting and student difficulty feedback to force rationalization and targeted template refinement, systematically reducing faithfulness errors (Wang et al., 22 Aug 2024).
• Feedback-awareness: LLF-Bench ensures agents must learn genuinely from diverse and randomized textual feedback, rather than overfitting fixed prompts or reward patterns (Cheng et al., 2023).

5. Applications Across Domains

Interactive learning is validated in multiple settings:

• Language Acquisition: Trial-and-demonstration (TnD) protocols yield accelerated word learning and practice-makes-perfect effects. Teacher demonstration choice modulates the student's efficiency, with demonstrative absence leading to delayed acquisition (Ma et al., 22 May 2024).
• Reasoning and Problem Solving: Multi-agent frameworks like ILR optimize individual LLM reasoning in math and code tasks. Adaptive cooperation/competition and peer-aware GRPO lead to consistent accuracy boosts over static baselines, up to +5% absolute on benchmarks (Lin et al., 30 Sep 2025).
• Social Intelligence: SOTOPIA-π utilizes GPT-4 as both social task generator and evaluator, combining behavior cloning and self-reinforcement to nearly saturate expert-level goal completion, with substantial safety gains (Wang et al., 13 Mar 2024).
• Storytelling and Concept Transfer: Interactive RL with cognitively motivated teacher feedback demonstrates high data efficiency and targeted skill gain (Martins et al., 19 Sep 2025).

6. Limitations, Design Tradeoffs, and Future Directions

Several open challenges and tradeoffs are acknowledged:

• Supervision vs. Interaction Balance: Excessive imitation undermines adaptivity; pure trial-and-error can fail by itself without sufficient model initialization or supervision (Zhang et al., 2017).
• Scaling and Curriculum: Current frameworks often limit interaction to single or few turns, or modest model scale; dynamic curricula and sustained turn-taking could further mirror human learning (Stöpler et al., 9 May 2025).
• Evaluator Bias and Robustness: Reliance on LLM-based automated scoring risks overfitting, miscalibration, and transfer to human judgments may remain imperfect (see SOTOPIA-π human vs. GPT-4 rating gaps) (Wang et al., 13 Mar 2024).
• Integration of Naturalistic Feedback: LLF-Bench sets standards for integrating suggestion, explanation, and performance reporting in feedback; open questions remain in value function estimation directly from text, adaptive policy stopping criteria, and meta-learning for paraphrased instructions (Cheng et al., 2023).

Potential extensions:

• Multi-turn interactive RL in environments with repair or clarification dialogue (Stöpler et al., 9 May 2025).
• Human-in-the-loop teachers or evaluators.
• Integration of RLHF and adaptive curriculum in real-time online training (Liang et al., 4 Mar 2024).
• Exploitation of multi-modal interaction and interactive vision-language learning (Grigsby et al., 6 May 2025).

Interactive learning frameworks in LMs provide precise mechanisms for leveraging ongoing, context-sensitive feedback across a wide range of domains and cognitive tasks. Empirical results validate notable gains in generalization, data efficiency, and robustness, with active research targeting remaining limitations and scalability.