4 papers · 1 filter
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4
Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training querie…
Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments
Maxwell Crouse, Ibrahim Abdelaziz, Kshitij Fadnis +6
Synthetic data has proven itself to be a valuable resource for tuning smaller, cost-effective language models to handle the complexities of multi-turn tool calling conversations. W…
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury +7
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there…
Self-Refinement of Language Models from External Proxy Metrics Feedback
Keshav Ramji, Young-Suk Lee, Ramón Fernandez Astudillo +5
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent respon…