What is recorded here?
Extracted short conversation segments for pause handling, turn-taking, backchannels and user interruptions and optimized axis-specific rewards with GRPO plus a semantic-quality judge reward.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits GRPO, existing speech models and prior interactivity alignment. Tested on Moshi and PersonaPlex; judge rewards and finite multi-turn tests do not prove preservation of meaning or universal interaction quality.
Read the original work
- Multi-Faceted Interactivity Alignment in Full-Duplex Speech Models
- Kyutai scientific publications index
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Atsumoto Ohashi; Neil Zeghidour; Alexandre Défossez; Eugene Kharitonov
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Multi-axis reinforcement learning for full-duplex interactivity
Extracted short conversation segments for pause handling, turn-taking, backchannels and user interruptions and optimized axis-specific rewards with GRPO plus a semantic-quality judge reward.
Scientific scope
Inherits GRPO, existing speech models and prior interactivity alignment. Tested on Moshi and PersonaPlex; judge rewards and finite multi-turn tests do not prove preservation of meaning or universal interaction quality.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Atsumoto Ohashi; Neil Zeghidour; Alexandre Défossez; Eugene Kharitonov
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.