REVIEW 7 cited by
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce \textbf{AgentOhana} as a comprehensive solution to address these challenges. \textit{AgentOhana} aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training. Leveraging the data unification, our training pipeline maintains equilibrium across different data sources and preserves independent randomness across devices during dataset partitioning and model training. Additionally, we present \textbf{xLAM-v0.1}, a large action model tailored for AI agents, which demonstrates exceptional performance across various benchmarks. Begin the exploration at \url{https://github.com/SalesforceAIResearch/xLAM}.
Forward citations
Cited by 7 Pith papers
-
NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability
NeSyFS combines a knowledge-graph belief state, a reflection step, and TSMC-style particle planning to improve LLM agent success rates on ALFWorld, WebShop, and ScienceWorld.
-
CurateEvo: Data-Curation Evolving for Agentic Post-Training
CurateEvo evolves executable data-curation code using failed agent trajectories, improving post-training performance by 3.2 and 2.7 points over baselines on labeled and wild data respectively.
-
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
MOAT alternately aligns a planning and a grounding LLM agent via perplexity-based DPO and self-generated SFT, reporting small but consistent gains over independently tuned baselines.
-
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
A self-evolving computer-use agent trained with full-trajectory state judging and curriculum task generation goes from 11.3% to 34.5% average success on five OSWorld apps, and a specialist-to-generalist variant beats ...
-
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
Self-generated agent trajectories from LAM SIMULATOR improved fine-tuned model pass rates by up to 49.3% on ToolBench and CRMArena.
-
AgentSLABench: Evaluating and Benchmarking Agentic Systems Under Resource Constraints
AgentSLABench adds resource budgets and cost/latency/memory profiles to AI agent evaluation, but its headline results rest on tiny samples and an internal inconsistency.
-
Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
The paper proposes that self-improving agents must learn to manage their own learning processes, framing this as intrinsic metacognitive learning, and argues it is necessary for sustained and generalized improvement.
Discussion (0). Continue with ORCID to comment.