One reward bit per rollout: does RL strengthen, recombine, or create reasoning circuits?
Gauri_the_great · x · 2026-10-17
A thread on why sparse RL feedback can be effective: one bit of reward per rollout looks weak, but the pretrained model may already produce the relevant reasoning strategy at low probability — sparse feedback could make that rare path reliable rather than constructing it from scratch.
The open mech interp question: does RL mainly strengthen the circuits behind the strategy, recombine them, or develop new ones? The distinction matters for understanding when RL can improve reliability versus when it can expand what the model is actually capable of.
More from Research
- Snorkel AI: 4B model beats 235B on financial analysis for under $500 with RL and verified data — ajratner · 2026-10-18
- tangermeme merges DeepLIFT/SHAP extension bringing attribution to arbitrary models — jmschreiber91 · 2026-10-18
- Tencent's Terminal-RSI lifts terminal agents from 20.7% to 50.6% on Terminal-Bench via recursive self-improvement — omarsar0 · 2026-10-18
- Shanahan baffled LLM anomalies get no mainstream attention; paper on existential LLM conversations — mpshanahan · 2026-10-17
- 10 minutes of ultrashort-wave stimulation boosted brain waste clearance in mice — rand_longevity · 2026-10-17
- MIT's IRIS predicts cell fate signals, verified in mouse embryo experiments — alex_verem · 2026-10-17