Training Composer for longer horizons

Composer, a specialized model designed for long-horizon tasks, employs a reinforcement learning process called self-summarization to improve its performance on complex coding challenges. This approach allows Composer to handle tasks that require extensive sequences of actions by summarizing its context when reaching a fixed token-length trigger, thus overcoming the limitations of compaction techniques that can cause loss of critical information. By integrating self-summarization into its training, Composer can efficiently condense context into high-value summaries with fewer tokens, significantly enhancing its performance in context-constrained environments. Testing against a baseline, Composer demonstrated superior results, reducing compaction errors by 50% while requiring only a fraction of the tokens. This capability enables Composer to tackle intricate problems, such as those in the Terminal-Bench 2.0, by condensing over 100,000 tokens into concise, actionable information. The ongoing development of Composer aims to extend its applicability to even more complex tasks, including multi-agent coordination, promising advancements in the field of agentic systems.

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