Search papers, labs, and topics across Lattice.
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Recursive belief updates in AgentOPSD reveal pivotal decision points, leading to a 89.1% success rate on complex RL tasks.
MERaLiON-GR outperforms existing models in gender recognition across multiple Southeast Asian languages, showcasing the power of specialized speech models.
PCSD boosts reinforcement learning performance by 15.6 points over existing methods, demonstrating that persistent teacher signals can effectively guide agents through sparse reward landscapes.
Bridging the gap between proprietary and open-source models, MAPD achieves up to 44.4% success in QA tasks by transforming sparse RL signals into dense distillation guidance.
SEED transforms past experiences into actionable skills, allowing reinforcement learning policies to evolve and improve in real-time.
LLM agents can internalize skills via in-context RL, achieving zero-shot autonomous behavior without the token overhead and retrieval noise of traditional methods.
