Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
DeepSeek-V2 delivers top-tier open-source LLM performance using only 21B active parameters by compressing the KV cache 93.3% and cutting training costs 42.5% via MLA and DeepSeekMoE.
TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.
Math-Shepherd is an automatically trained process reward model that scores solution steps to verify and reinforce LLMs, lifting Mistral-7B from 77.9% to 89.1% on GSM8K and 28.6% to 43.5% on MATH.
AgentRevive is a Markov state-aware framework with policy learning and edge optimization that manages Active, Standby, and Terminated agent states to enable resilient multi-agent evolution and reduce token consumption.
DeepSeek LLM 67B exceeds LLaMA-2 70B on code, mathematics and reasoning benchmarks after pre-training on 2 trillion tokens and alignment via SFT and DPO.
citing papers explorer
-
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
-
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
DeepSeek-V2 delivers top-tier open-source LLM performance using only 21B active parameters by compressing the KV cache 93.3% and cutting training costs 42.5% via MLA and DeepSeekMoE.
-
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
TRN-R1-Zero is an RL-only post-training method that lets LLMs perform zero-shot node, edge, and graph reasoning on text-rich networks without supervised data or larger-model distillation.
-
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Math-Shepherd is an automatically trained process reward model that scores solution steps to verify and reinforce LLMs, lifting Mistral-7B from 77.9% to 89.1% on GSM8K and 28.6% to 43.5% on MATH.
-
Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution
AgentRevive is a Markov state-aware framework with policy learning and edge optimization that manages Active, Standby, and Terminated agent states to enable resilient multi-agent evolution and reduce token consumption.
-
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
DeepSeek LLM 67B exceeds LLaMA-2 70B on code, mathematics and reasoning benchmarks after pre-training on 2 trillion tokens and alignment via SFT and DPO.
- Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection