rlvr
for reinforcement learning engineers
Why this scored 4
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 119
Boosting LLM Exploration via Weak-Model Guidance in RLVR
Boosts LLM exploration using weak-model guidance within RLVR framework. · for LLM researchers
arxivSteadyai48d ago - 219
Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms
Analyzes fusion paradigms to consolidate RLVR capabilities across domains. · for AI research leads
arxivSteadyai48d ago - 315
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
Introduces a rollout-based credit method for RL without group sampling. · for reinforcement learning engineers
arxivSteadyai57m ago