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[2503.04940] VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantization
Summary
The paper presents VQEL, a novel architecture that enhances self-play in emergent language games through agent-internal vector quantization, improving communication protocol learning among artificial agents.
Why It Matters
This research addresses the challenges of learning discrete communication protocols in artificial agents, a crucial aspect of advancing AI's ability to communicate effectively. By introducing self-play as a method for language emergence, it opens new avenues for developing more robust AI systems capable of complex interactions.
Key Takeaways
- VQEL incorporates vector quantization to enhance message generation in emergent language games.
- Self-play is proposed as an effective method for agents to learn discrete communication protocols.
- Empirical results indicate improved symbol alignment and task success in agents pre-trained with VQEL.
- The architecture maintains end-to-end differentiability, addressing optimization challenges.
- This work contributes to the understanding of emergent communication in AI systems.
Computer Science > Computation and Language arXiv:2503.04940 (cs) [Submitted on 6 Mar 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantization Authors:Mohammad Mahdi Samiei Paqaleh, Mehdi Jamalkhah, Mahdieh Soleymani Baghshah View a PDF of the paper titled VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantization, by Mohammad Mahdi Samiei Paqaleh and 2 other authors View PDF HTML (experimental) Abstract:Emergent Language (EL) focuses on the emergence of communication among artificial agents. Although symbolic communication channels more closely mirror the discrete nature of human language, learning such protocols remains fundamentally difficult due to the non-differentiability of symbol sampling. Existing approaches typically rely on high-variance gradient estimators such as REINFORCE or on continuous relaxations such as Gumbel-Softmax, both of which suffer from limitations in training stability and scalability. Motivated by cognitive theories that emphasize intrapersonal processes preceding communication, we explore self-play as a substrate for language emergence prior to mutual interaction. We introduce Vector Quantized Emergent Language (VQEL), a novel architecture that incorporates vector quantization into the message generation process. VQEL enables agents to perform self-play using discrete internal representations derived from a learned co...