Yash Lara
Senior PM Lead in the AI Frontiers lab at Microsoft Research, focused on launching AI products and translating research insights into product innovations across human-AI interaction, product management, program management, and machine learning.
Energy-Based Fine-Tuning Trains Language Models on Whole Responses
Microsoft Research’s presentation on energy-based fine-tuning argues that language-model post-training can be aimed at whole responses rather than next-token imitation. Carles Domingo-Enrich presents EBFT as a middle path between supervised fine-tuning and reinforcement learning: it samples model completions, compares them with ground-truth answers in a model-derived feature space, and turns that comparison into a policy-gradient update without a separate reward model or verifier. The reported results show gains over SFT on several coding and translation measures, with performance often comparable to RLVR while avoiding explicit correctness rewards.
AI’s Biggest Disruption Requires Rebuilding Markets Around Agents
David Rothschild argues that AI’s largest economic effects will come less from better models than from whether workflows and markets are rebuilt for agents rather than humans. In his Microsoft Research Forum talk and related work on agentic markets, he says the key question is architectural: open systems could reduce communication friction and spread welfare gains, while closed platforms could use the same capabilities to reinforce incumbency. The transition, in his account, depends on choices about delegation, monitoring, auditability, and market access that are being made before the full disruption is visible.
Interwhen Verifies AI Agent Actions Before They Become Irreversible
Microsoft Research’s Amit Sharma presents Interwhen as a framework for moving AI agents from post-hoc checking to verified execution while they are still acting. The open-source library uses LLMs to turn natural-language instructions, policies, and partial responses into smaller verifiable properties, then applies symbolic or model-based verifiers to tool calls and intermediate behavior. Sharma argues that this lets agents continue normally when checks pass but interrupts them when a verifier detects a violation, addressing risks that final-output review may catch too late.
GitHub Agentic Workflows Turn Actions Into AI-Run Development Processes
Microsoft Research’s Peli Halleux and Yash Lara present GitHub Agentic Workflows as a move from AI-assisted coding to repository-level process automation. Their argument is that agents should be embedded inside GitHub Actions to research, plan, assign, and open pull requests under human review, rather than operate as unconstrained swarms. The system’s promised scale depends on orchestration, sandboxing, limited permissions, and Microsoft-hosted models on Azure.