LExGraph advances exploration memory
LExGraph organizes exploration dialogues into graphs for superior long-term memory.
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LExGraph organizes exploration dialogues into graphs for superior long-term memory.
A shared classifier partitions the search space so lower-ranked models cover regions higher-ranked ones miss.
Across 11 environments it reports about 2x returns, about 100x fewer interactions than RL, and +40% real-robot success.
Across eight leading models, GPT-6 Sol reaches only 61.9% calibrated accuracy when rules stay hidden and can change.
EDEN adds anchored embedding noise to RLVR rollouts, improving exploration and solution coverage over GRPO.
Exploration-Distillation separates novelty search from RLVR optimization and beats DAPO on math benchmarks.
RoboQuest finds frontier physical agents succeed on only 23% of exploration tasks.
A formal criterion shows common intrinsic rewards can fail to produce optimally informative exploration.
DexPolicy anneals exploration for dexterous manipulation, raising target success for PPO, GRPO, and FPO.
ExplorationBench evaluates AI systems' scientific exploration in verifiable 'Alien Worlds' sandboxes where recall cannot solve tasks; top systems still stall or reverse gains.
ActObs adds observation-token supervision to SFT, yielding higher pass@k for Qwen3 agents after GRPO on Terminal-Bench 2.0 and code editing.
Dream-RSI refines exploration policies by dreaming in replay simulators built from discovery history, cutting discovery costs across coding tasks.
Separating design-direction exploration from code generation via structured specifications broadens UI alternatives without destabilizing output.
MInTRL injects sparse judge corrections into on-policy RL rollouts, expanding exploration beyond on-policy sampling while preserving learnability on math and code benchmarks.

New DiG-bench benchmark of 70 hidden-rule games shows only Opus 5 and Fable 5 solving the hardest tiers, probing AI discovery and creativity.
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