SPACE cuts agent LLM calls by 78.9% while raising success rate on long-horizon tasks
dair_ai · x · 2026-09-04
SPACE tackles a core inefficiency of ReAct-style agents: one primitive action per model round means most of a long-horizon episode is spent re-deciding routine sequences.
- Training variable-length action chunks with vanilla RL fails because the policy never learns where a chunk should end.
- SPACE induces two-level programmatic skills from successful trajectories and uses subskill boundaries as direct chunk-boundary labels.
- It distills this temporal structure via hybrid on/off-policy optimization with chunk-aware credit assignment.
Result: 78.9% fewer LLM calls with higher success rate.
Related event: SPACE Cuts Agent LLM Calls by 78.9% While Boosting Success(3 posts)→
More from coding & agent
- JSON prompting with MOSS Image 5B: bbox coordinates for exact multi-room layouts — cocktailpeanut · 2026-10-21
- Rewriting an agent skill: A/B challenge runs show skills should only add what agents don't know — jmschreiber91 · 2026-10-21
- Developer builds himself a 'Rork MoTS' agent on Grok Bot, aiming for full autonomy before GTA 6 — rudrank · 2026-10-21
- OpenAI Live API tip: control voice sessions from your backend via sideband WebSocket — pbbakkum · 2026-10-21
- OpenAI's MultiLangBench: PHP scores highest (91.3%) at building a database engine from scratch — ricklamers · 2026-10-21
- UserProof lets Claude Code watch your user session recordings and boost conversion for $29/mo — doooyle · 2026-10-21